@comment{{This file has been generated by bib2bib 1.91}}
@comment{{Command line: /usr/bin/bib2bib -c '$key <> "R:Ligges:2005"' -c '$type = "book"' R.bib}}
@book{R:Becker+Chambers+Wilks:1988,
author = {Richard A. Becker and John M. Chambers and Allan
R. Wilks},
title = {The New {S} Language},
publisher = {Chapman \& Hall},
year = 1988,
address = {London},
abstract = {This book is often called the ``\emph{Blue Book}'',
and introduced what is now known as S version 2.}
}
@book{R:Chambers+Hastie:1992,
author = {John M. Chambers and Trevor J. Hastie},
title = {Statistical Models in {S}},
publisher = {Chapman \& Hall},
year = 1992,
address = {London},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3040&parent_id=&pc=},
abstract = {This is also called the ``\emph{White Book}'', and
introduced S version 3, which added structures to
facilitate statistical modeling in S.},
orderinfo = {crcpress.txt}
}
@book{R:Chambers:1998,
author = {John M. Chambers},
title = {Programming with Data},
publisher = {Springer},
year = 1998,
address = {New York},
note = {ISBN 0-387-98503-4},
url = {http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2008951-0,00.html},
abstract = {This ``\emph{Green Book}'' describes version 4 of S, a
major revision of S designed by John Chambers to
improve its usefulness at every stage of the
programming process.},
orderinfo = {springer.txt}
}
@book{R:Venables+Ripley:2002,
author = {William N. Venables and Brian D. Ripley},
title = {Modern Applied Statistics with {S}. Fourth Edition},
publisher = {Springer},
year = 2002,
address = {New York},
note = {ISBN 0-387-95457-0},
url = {http://www.stats.ox.ac.uk/pub/MASS4/},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-1542120-0,00.html},
abstract = {A highly recommended book on how to do statistical
data analysis using R or S-Plus. In the first
chapters it gives an introduction to the S language.
Then it covers a wide range of statistical
methodology, including linear and generalized linear
models, non-linear and smooth regression, tree-based
methods, random and mixed effects, exploratory
multivariate analysis, classification, survival
analysis, time series analysis, spatial statistics,
and optimization. The `on-line complements' available
at the books homepage provide updates of the book, as
well as further details of technical material. },
orderinfo = {springer.txt}
}
@book{R:Venables+Ripley:2000,
author = {William N. Venables and Brian D. Ripley},
title = {S Programming},
publisher = {Springer},
year = 2000,
address = {New York},
note = {ISBN 0-387-98966-8},
url = {http://www.stats.ox.ac.uk/pub/MASS3/Sprog/},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2104231-0,00.html},
abstract = {This provides an in-depth guide to writing software in
the S language which forms the basis of both the
commercial S-Plus and the Open Source R data analysis
software systems.},
orderinfo = {springer.txt}
}
@book{R:Nolan+Speed:2000,
author = {Deborah Nolan and Terry Speed},
title = {Stat Labs: Mathematical Statistics Through
Applications},
publisher = {Springer},
year = 2000,
series = {Springer Texts in Statistics},
note = {ISBN 0-387-98974-9},
url = {http://www.stat.Berkeley.EDU/users/statlabs/},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40106-22-2104508-0,00.html?changeHeader=true},
abstract = {Integrates theory of statistics with the practice of
statistics through a collection of case studies
(``labs''), and uses R to analyze the data.},
orderinfo = {springer.txt}
}
@book{R:Pinheiro+Bates:2000,
author = {Jose C. Pinheiro and Douglas M. Bates},
title = {Mixed-Effects Models in {S} and {S-Plus}},
publisher = {Springer},
year = 2000,
note = {ISBN 0-387-98957-0},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10129-22-2102822-0,00.html?changeHeader=true},
abstract = {A comprehensive guide to the use of the `nlme' package
for linear and nonlinear mixed-effects models.},
orderinfo = {springer.txt}
}
@book{R:Harrell:2001,
author = {Frank E. Harrell},
title = {Regression Modeling Strategies, with Applications to
Linear Models, Survival Analysis and Logistic
Regression},
publisher = {Springer},
year = 2001,
note = {ISBN 0-387-95232-2},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-0-22-2187282-0,00.html},
url = {http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RmS},
abstract = {There are many books that are excellent sources of
knowledge about individual statistical tools (survival
models, general linear models, etc.), but the art of
data analysis is about choosing and using multiple
tools. In the words of Chatfield ``... students
typically know the technical details of regression for
example, but not necessarily when and how to apply it.
This argues the need for a better balance in the
literature and in statistical teaching between
techniques and problem solving strategies.'' Whether
analyzing risk factors, adjusting for biases in
observational studies, or developing predictive
models, there are common problems that few regression
texts address. For example, there are missing data in
the majority of datasets one is likely to encounter
(other than those used in textbooks!) but most
regression texts do not include methods for dealing
with such data effectively, and texts on missing data
do not cover regression modeling.},
orderinfo = {springer.txt}
}
@book{R:Limas+Mere+Juez:2001,
author = {Manuel Castej{\'o}n Limas and Joaqu{\'\i}n Ordieres
Mer{\'e} and Fco. Javier de Cos Juez and Fco. Javier
Mart{\'\i}nez de Pis{\'o}n Ascacibar},
title = {Control de Calidad. Metodologia para el analisis
previo a la modelizaci{\'o}n de datos en procesos
industriales. Fundamentos te{\'o}ricos y aplicaciones
con R.},
publisher = {Servicio de Publicaciones de la Universidad de La
Rioja},
year = 2001,
note = {ISBN 84-95301-48-2},
abstract = {This book, written in Spanish, is oriented to
researchers interested in applying multivariate
analysis techniques to real processes. It combines
the theoretical basis with applied examples coded in
R.}
}
@book{R:Fox:2002,
author = {John Fox},
title = {An {R} and {S-Plus} Companion to Applied Regression},
publisher = {Sage Publications},
year = 2002,
address = {Thousand Oaks, CA, USA},
note = {ISBN 0-761-92279-2},
url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html},
abstract = {A companion book to a text or course on applied
regression (such as ``Applied Regression, Linear
Models, and Related Methods'' by the same author). It
introduces S, and concentrates on how to use linear
and generalized-linear models in S while assuming
familiarity with the statistical methodology.}
}
@book{R:Dalgaard:2002,
author = {Peter Dalgaard},
title = {Introductory Statistics with {R}},
year = 2002,
publisher = {Springer},
note = {ISBN 0-387-95475-9},
pages = 288,
url = {http://www.biostat.ku.dk/~pd/ISwR.html},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10130-22-2287329-0,00.html?changeHeader=true},
orderinfo = {springer.txt}
}
@book{R:Iacus+Masarotto:2003,
author = {Stefano Iacus and Guido Masarotto},
title = {Laboratorio di statistica con {R}},
year = 2003,
publisher = {McGraw-Hill},
address = {Milano},
note = {ISBN 88-386-6084-0},
pages = 384,
publisherurl = {http://www.ateneonline.it/LibroAteneo.asp?item_id=1436}
}
@book{R:Maindonald+Braun:2003,
author = {John Maindonald and John Braun},
title = {Data Analysis and Graphics Using {R}},
year = 2003,
publisher = {Cambridge University Press},
address = {Cambridge},
note = {ISBN 0-521-81336-0},
pages = 362,
url = {http://wwwmaths.anu.edu.au/~johnm/r-book.html},
publisherurl = {http://www.cup.org/}
}
@book{R:Parmigiani+Garrett+Irizarry+Zeger:2003,
author = {Giovanni Parmigiani and Elizabeth S. Garrett and
Rafael A. Irizarry and Scott L. Zeger},
title = {The Analysis of Gene Expression Data},
publisher = {Springer},
year = 2003,
address = {New York},
note = {ISBN 0-387-95577-1},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2292983-0,00.html},
orderinfo = {springer.txt}
}
@book{R:Huet+Bouvier+Gruet+Jolivet:2003,
author = {Sylvie Huet and Annie Bouvier and Marie-Anne Gruet and
Emmanuel Jolivet},
title = {Statistical Tools for Nonlinear Regression},
publisher = {Springer},
year = 2003,
address = {New York},
note = {ISBN 0-387-40081-8},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-7107970-0,00.html},
orderinfo = {springer.txt}
}
@book{R:Mase+Kamakura+Jimbo:2004,
author = {S. Mase and T. Kamakura and M. Jimbo and K. Kanefuji},
title = {Introduction to Data Science for engineers--- Data
analysis using free statistical software {R} (in
Japanese)},
publisher = {Suuri-Kogaku-sha, Tokyo},
year = 2004,
month = {April},
note = {ISBN 4901683128},
pages = 254
}
@book{R:Faraway:2004,
author = {Julian J. Faraway},
title = {Linear Models with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2004,
address = {Boca Raton, FL},
note = {ISBN 1-584-88425-8},
url = {http://www.maths.bath.ac.uk/~jjf23/LMR/},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4258&parent_id=&pc=},
abstract = {The book focuses on the practice of regression and
analysis of variance. It clearly demonstrates the
different methods available and in which situations
each one applies. It covers all of the standard
topics, from the basics of estimation to missing data,
factorial designs, and block designs, but it also
includes discussion of topics, such as model
uncertainty, rarely addressed in books of this type.
The presentation incorporates an abundance of examples
that clarify both the use of each technique and the
conclusions one can draw from the results.},
orderinfo = {crcpress.txt}
}
@book{R:Heiberger+Holland:2004,
author = {Richard M. Heiberger and Burt Holland},
title = {Statistical Analysis and Data Display: An Intermediate
Course with Examples in {S-Plus}, {R}, and {SAS}},
publisher = {Springer},
year = 2004,
series = {Springer Texts in Statistics},
note = {ISBN 0-387-40270-5},
url = {http://astro.temple.edu/~rmh/HH},
abstract = {A contemporary presentation of statistical methods
featuring 200 graphical displays for exploring data
and displaying analyses. Many of the displays appear
here for the first time. Discusses construction and
interpretation of graphs, principles of graphical
design, and relation between graphs and traditional
tabular results. Can serve as a graduate-level
standalone statistics text and as a reference book for
researchers. In-depth discussions of regression
analysis, analysis of variance, and design of
experiments are followed by introductions to analysis
of discrete bivariate data, nonparametrics, logistic
regression, and ARIMA time series modeling. Concepts
and techniques are illustrated with a variety of case
studies. S-Plus, R, and SAS executable functions are
provided and discussed. S functions are provided for
each new graphical display format. All code,
transcript and figure files are provided for readers
to use as templates for their own analyses.},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10129-22-28904982-0,00.html?changeHeader=true},
orderinfo = {springer.txt}
}
@book{R:Verzani:2005,
author = {John Verzani},
title = {Using {R} for Introductory Statistics},
publisher = {Chapman \& Hall/CRC},
year = 2005,
address = {Boca Raton, FL},
note = {ISBN 1-584-88450-9},
url = {http://wiener.math.csi.cuny.edu/UsingR/},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4509&parent_id=&pc=},
abstract = {There are few books covering introductory statistics
using R, and this book fills a gap as a true
``beginner'' book. With emphasis on data analysis and
practical examples, `Using R for Introductory
Statistics' encourages understanding rather than
focusing on learning the underlying theory. It
includes a large collection of exercises and numerous
practical examples from a broad range of scientific
disciplines. It comes complete with an online
resource containing datasets, R functions, selected
solutions to exercises, and updates to the latest
features. A full solutions manual is available from
Chapman \& Hall/CRC.},
orderinfo = {crcpress.txt}
}
@book{R:Murtagh:2005,
author = {Fionn Murtagh},
title = {Correspondence Analysis and Data Coding with {JAVA}
and {R}},
publisher = {Chapman \& Hall/CRC},
year = 2005,
address = {Boca Raton, FL},
note = {ISBN 1-584-88528-9},
url = {http://www.cs.rhul.ac.uk/home/fionn/},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C5289&parent_id=&pc=},
abstract = {This book provides an introduction to methods and
applications of correspondence analysis, with an
emphasis on data coding --- the first step in
correspondence analysis. It features a practical
presentation of the theory with a range of
applications from data mining, financial engineering,
and the biosciences. Implementation of the methods is
presented using JAVA and R software.},
orderinfo = {crcpress.txt}
}
@book{R:Murrell:2005,
author = {Paul Murrell},
title = {R Graphics},
publisher = {Chapman \& Hall/CRC},
year = 2005,
address = {Boca Raton, FL},
note = {ISBN 1-584-88486-X},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C486X&parent_id=&pc=},
url = {http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html},
abstract = {A description of the core graphics features of R
including: a brief introduction to R; an introduction
to general R graphics features. The ``base'' graphics
system of R: traditional S graphics. The power and
flexibility of grid graphics. Building on top of the
base or grid graphics: Trellis graphics and
developing new graphics functions.},
orderinfo = {crcpress.txt}
}
@book{R:Crawley:2005,
author = {Michael J. Crawley},
title = {Statistics: An Introduction using {R}},
publisher = {Wiley},
year = 2005,
note = {ISBN 0-470-02297-3},
publisherurl = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470022973.html},
url = {http://www.bio.ic.ac.uk/research/crawley/statistics/},
abstract = {The book is primarily aimed at undergraduate
students in medicine, engineering, economics and
biology --- but will also appeal to postgraduates who
have not previously covered this area, or wish to
switch to using R.}
}
@book{R:Everitt:2005,
author = {Brian S. Everitt},
title = {An {R} and {S-Plus} Companion to Multivariate Analysis},
publisher = {Springer},
year = 2005,
note = {ISBN 1-85233-882-2},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-34953445-0,00.html},
url = {http://biostatistics.iop.kcl.ac.uk/publications/everitt/},
abstract = {In this book the core multivariate methodology is
covered along with some basic theory for each method
described. The necessary R and S-Plus code is given
for each analysis in the book, with any differences
between the two highlighted.},
orderinfo = {springer.txt}
}
@book{R:Deonier+Tavare+Waterman:2005,
author = {Richard C. Deonier and Simon Tavar{\'e} and Michael
S. Waterman},
title = {Computational Genome Analysis: An Introduction},
publisher = {Springer},
year = 2005,
note = {ISBN: 0-387-98785-1},
publisherurl = {http://www.springeronline.com/0-387-98785-1},
abstract = {Computational Genome Analysis: An Introduction
presents the foundations of key p roblems in
computational molecular biology and bioinformatics. It
focuses on com putational and statistical principles
applied to genomes, and introduces the mat hematics
and statistics that are crucial for understanding
these applications. A ll computations are done with
R.},
orderinfo = {springer.txt}
}
@book{R:Gentleman+Carey+Huber:2005,
editor = {Robert Gentleman and Vince Carey and Wolfgang Huber
and Rafael Irizarry and Sandrine Dudoit},
title = {Bioinformatics and Computational Biology Solutions
Using {R} and {Bioconductor}},
publisher = {Springer},
year = 2005,
series = {Statistics for Biology and Health},
note = {ISBN: 0-387-25146-4},
publisherurl = {http://www.springeronline.com/0-387-25146-4},
abstract = {This volume's coverage is broad and ranges across most
of the key capabilities of the Bioconductor project,
including importation and preprocessing of
high-throughput data from microarray, proteomic, and
flow cytometry platforms.},
orderinfo = {springer.txt}
}
@book{R:Therneau+Grambsch:2000,
author = {Terry M. Therneau and Patricia M. Grambsch},
title = {Modeling Survival Data: Extending the {Cox} Model},
publisher = {Springer},
year = 2000,
series = {Statistics for Biology and Health},
note = {ISBN: 0-387-98784-3},
publisherurl = {http://www.springeronline.com/0-387-98784-3},
abstract = {This is a book for statistical practitioners,
particularly those who design and analyze studies for
survival and event history data. Its goal is to extend
the toolkit beyond the basic triad provided by most
statistical packages: the Kaplan-Meier estimator,
log-rank test, and Cox regression model.},
orderinfo = {springer.txt}
}
@book{R:Everitt+Hothorn:2006,
author = {Brian Everitt and Torsten Hothorn},
title = {A Handbook of Statistical Analyses Using {R}},
publisher = {Chapman \& Hall/CRC},
year = 2006,
address = {Boca Raton, FL},
note = {ISBN 1-584-88539-4},
url = {http://cran.r-project.org/src/contrib/Descriptions/HSAUR.html},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C5394&parent_id=&pc=},
abstract = {With emphasis on the use of R and the interpretation
of results rather than the theory behind the methods,
this book addresses particular statistical techniques
and demonstrates how they can be applied to one or
more data sets using R. The authors provide a concise
introduction to R, including a summary of its most
important features. They cover a variety of topics,
such as simple inference, generalized linear models,
multilevel models, longitudinal data, cluster
analysis, principal components analysis, and
discriminant analysis. With numerous figures and
exercises, A Handbook of Statistical Analysis using R
provides useful information for students as well as
statisticians and data analysts.},
orderinfo = {crcpress.txt}
}
@book{R:Faraway:2006,
author = {Julian J. Faraway},
title = {Extending Linear Models with {R}: Generalized Linear,
Mixed Effects and Nonparametric Regression Models},
publisher = {Chapman \& Hall/CRC},
year = 2006,
address = {Boca Raton, FL},
note = {ISBN 1-584-88424-X},
url = {http://www.maths.bath.ac.uk/~jjf23/ELM/},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C424X&parent_id=&pc=},
abstract = {This book surveys the techniques that grow from the
regression model, presenting three extensions to that
framework: generalized linear models (GLMs), mixed
effect models, and nonparametric regression
models. The author's treatment is thoroughly modern
and covers topics that include GLM diagnostics,
generalized linear mixed models, trees, and even the
use of neural networks in statistics. To demonstrate
the interplay of theory and practice, throughout the
book the author weaves the use of the R software
environment to analyze the data of real examples,
providing all of the R commands necessary to reproduce
the analyses.},
orderinfo = {crcpress.txt}
}
@book{R:Jureckova+Picek:2006,
author = {Jana Jureckova and Jan Picek},
title = {Robust Statistical Methods with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2006,
address = {Boca Raton, FL},
note = {ISBN 1-584-88454-1},
url = {http://www.fp.vslib.cz/kap/picek/robust/},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4541&parent_id=&pc=},
abstract = {This book provides a systematic treatment of robust
procedures with an emphasis on practical application.
The authors work from underlying mathematical tools to
implementation, paying special attention to the
computational aspects. They cover the whole range of
robust methods, including differentiable statistical
functions, distance of measures, influence functions,
and asymptotic distributions, in a rigorous yet
approachable manner. Highlighting hands- on problem
solving, many examples and computational algorithms
using the R software supplement the discussion. The
book examines the characteristics of robustness,
estimators of real parameter, large sample properties,
and goodness-of-fit tests. It also includes a brief
overview of R in an appendix for those with little
experience using the software.},
orderinfo = {crcpress.txt}
}
@book{R:Wood:2006,
author = {Simon N. Wood},
title = {Generalized Additive Models: An Introduction with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2006,
address = {Boca Raton, FL},
note = {ISBN 1-584-88474-6},
url = {http://cran.r-project.org/src/contrib/Descriptions/gamair.html},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4746&parent_id=&pc=},
abstract = {This book imparts a thorough understanding of the
theory and practical applications of GAMs and related
advanced models, enabling informed use of these very
flexible tools. The author bases his approach on a
framework of penalized regression splines, and builds
a well- grounded foundation through motivating
chapters on linear and generalized linear models.
While firmly focused on the practical aspects of GAMs,
discussions include fairly full explanations of the
theory underlying the methods. The treatment is rich
with practical examples, and it includes an entire
chapter on the analysis of real data sets using R and
the author's add-on package mgcv. Each chapter
includes exercises, for which complete solutions are
provided in an appendix.},
orderinfo = {crcpress.txt}
}
@book{R:Pfaff:2006,
author = {Bernhard Pfaff},
title = {Analysis of Integrated and Cointegrated Time Series
with {R}},
publisher = {Springer},
year = 2006,
series = {Use R},
note = {ISBN 0-387-98784-3},
publisherurl = {http://www.springeronline.com/0-387-27959-8},
abstract = {The book encompasses seasonal unit roots, fractional
integration, coping with structural breaks, and
inference in cointegrated vector autoregressive
models.},
orderinfo = {springer.txt}
}
@book{R:Le+Zidek:2006,
author = {Nhu D. Le and James V. Zidek},
title = {Statistical Analysis of Environmental Space-Time
Processes},
publisher = {Springer},
year = 2006,
note = {ISBN 0-387-26209-1},
publisherurl = {http://www.springer.com/0-387-26209-1},
abstract = {This book provides a broad introduction to the subject
of environmental space-time processes, addressing the
role of uncertainty. It covers a spectrum of technical
matters from measurement to environmental epidemiology
to risk assessment. It showcases non-stationary
vector-valued processes, while treating stationarity
as a special case. In particular, with members of
their research group the authors developed within a
hierarchical Bayesian framework, the new statistical
approaches presented in the book for analyzing,
modeling, and monitoring environmental spatio-temporal
processes. Furthermore they indicate new directions
for development.},
orderinfo = {springer.txt}
}
@book{R:Diggle+Ribeiro:2006,
author = {Peter J. Diggle and Paulo Justiniano Ribeiro},
title = {Model-based Geostatistics},
publisher = {Springer},
year = 2006,
note = {ISBN 0-387-32907-2},
publisherurl = {http://www.springer.com/0-387-32907-2},
abstract = {Geostatistics is concerned with estimation and
prediction problems for spatially continuous
phenomena, using data obtained at a limited number of
spatial locations. The name reflects its origins in
mineral exploration, but the methods are now used in a
wide range of settings including public health and the
physical and environmental sciences. Model-based
geostatistics refers to the application of general
statistical principles of modeling and inference to
geostatistical problems. This volume is the first
book-length treatment of model-based geostatistics.},
orderinfo = {springer.txt}
}
@book{R:Paradis:2006,
author = {Emmanuel Paradis},
title = {Analysis of Phylogenetics and Evolution with {R}},
publisher = {Springer},
year = 2006,
series = {Use R},
address = {New York},
note = {ISBN 0-387-32914-5},
publisherurl = {http://www.springer.com/0-387-32914-5},
abstract = {This book integrates a wide variety of data analysis
methods into a single and flexible interface: the R
language, an open source language is available for a
wide range of computer systems and has been adopted as
a computational environment by many authors of
statistical software. Adopting R as a main tool for
phylogenetic analyses sease the workflow in
biologists' data analyses, ensure greater scientific
repeatability, and enhance the exchange of ideas and
methodological developments.},
orderinfo = {springer.txt}
}
@book{R:Dudoit+Laan:2007,
author = {Sandrine Dudoit and Mark J. {van der Laan}},
title = {Multiple Testing Procedures and Applications to
Genomics},
publisher = {Springer},
year = 2007,
series = {Springer Series in Statistics},
note = {ISBN: 978-0-387-49316-9},
publisherurl = {http://www.springeronline.com/978-0-387-49316-9},
abstract = {This book provides a detailed account of the
theoretical foundations of proposed multiple testing
methods and illustrates their application to a range
of testing problems in genomics.},
orderinfo = {springer.txt}
}
@book{R:Ligges:2007,
author = {Uwe Ligges},
title = {Programmieren mit {R}},
year = 2007,
publisher = {Springer-Verlag},
address = {Heidelberg},
note = {ISBN 3-540-36332-7, in German},
edition = {2nd},
url = {http://www.statistik.uni-dortmund.de/~ligges/PmitR/},
publisherurl = {http://www.springer.de/3-540-36332-7},
abstract = {R ist eine objekt-orientierte und interpretierte
Sprache und Programmierumgebung f\"ur Datenanalyse und
Grafik --- frei erh\"altlich unter der GPL. Das Buch
f\"uhrt in die Grundlagen der Sprache R ein und
vermittelt ein umfassendes Verst\"andnis der
Sprachstruktur. Die enormen Grafikf\"ahigkeiten von R
werden detailliert beschrieben. Der Leser kann leicht
eigene Methoden umsetzen, Objektklassen definieren und
ganze Pakete aus Funktionen und zugeh\"origer
Dokumentation zusammenstellen. Ob Diplomarbeit,
Forschungsprojekte oder Wirtschaftsdaten, das Buch
unterst\"utzt alle, die R als flexibles Werkzeug zur
Datenanalyse und -visualisierung einsetzen m\"ochten.},
language = {de}
}
@book{R:Dolic:2004,
author = {Dubravko Dolic},
title = {Statistik mit {R}. Einf\"uhrung f\"ur Wirtschafts-
und Sozialwissenschaftler},
year = 2004,
publisher = {R. Oldenbourg},
address = {M\"unchen, Wien},
note = {ISBN 3-486-27537-2, in German},
isbn = {3-486-27537-2},
language = {de}
}
@book{R:Behr:2005,
author = {Andreas Behr},
title = {Einf\"uhrung in die Statistik mit {R}},
series = {WiSo Kurzlehrb\"ucher},
year = 2005,
publisher = {Vahlen},
address = {M\"unchen},
note = {ISBN 3-8006-3219-5, in German},
isbn = {3-8006-3219-5},
language = {de}
}
@book{R:Lynch:2007,
author = {Scott M. Lynch},
title = {Introduction to Applied Bayesian Statistics and
Estimation for Social Scientists},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-71264-2},
publisherurl = {http://www.springer.com/978-0-387-71264-2},
abstract = {Introduction to Bayesian Statistics and Estimation for
Social Scientists covers the complete process of
Bayesian statistical analysis in great detail from the
development of a model through the process of making
statistical inference. The key feature of this book
is that it covers models that are most commonly used
in social science research-including the linear
regression model, generalized linear models,
hierarchical models, and multivariate regression
models-and it thoroughly develops each real-data
example in painstaking detail. },
orderinfo = {springer.txt}
}
@book{R:Albert:2007,
author = {Jim Albert},
title = {Bayesian Computation with {R}},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-71384-7},
publisherurl = {http://www.springer.com/978-0-387-71384-7},
abstract = {Bayesian Computation with R introduces Bayesian
modeling by the use of computation using the R
language. The early chapters present the basic tenets
of Bayesian thinking by use of familiar one and
two-parameter inferential problems. Bayesian
computational methods such as Laplace's method,
rejection sampling, and the SIR algorithm are
illustrated in the context of a random effects model.
The construction and implementation of Markov Chain
Monte Carlo (MCMC) methods is introduced. These
simulation-based algorithms are implemented for a
variety of Bayesian applications such as normal and
binary response regression, hierarchical modeling,
order-restricted inference, and robust modeling.
Algorithms written in R are used to develop Bayesian
tests and assess Bayesian models by use of the
posterior predictive distribution. The use of R to
interface with WinBUGS, a popular MCMC computing
language, is described with several illustrative
examples.},
orderinfo = {springer.txt}
}
@book{R:Marin+Robert:2007,
author = {Jean-Michel Marin and Christian P. Robert},
title = {Bayesian Core: A Practical Approach to Computational
Bayesian Statistics},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-38979-0},
publisherurl = {http://www.springer.com/978-0-387-38979-0},
abstract = {This Bayesian modeling book is intended for
practitioners and applied statisticians looking for a
self-contained entry to computational Bayesian
statistics. Focusing on standard statistical models
and backed up by discussed real datasets available
from the book website, it provides an operational
methodology for conducting Bayesian inference, rather
than focusing on its theoretical justifications.
Special attention is paid to the derivation of prior
distributions in each case and specific reference
solutions are given for each of the models.
Similarly, computational details are worked out to
lead the reader towards an effective programming of
the methods given in the book. While R programs are
provided on the book website and R hints are given in
the computational sections of the book, The Bayesian
Core requires no knowledge of the R language and it
can be read and used with any other programming
language. },
orderinfo = {springer.txt}
}
@book{R:Cook+Swayne:2007,
author = {Dianne Cook and Deborah F. Swayne},
title = {Interactive and Dynamic Graphics for Data Analysis},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-71761-6},
publisherurl = {http://www.springer.com/978-0-387-71761-6},
abstract = {This richly illustrated book describes the use of
interactive and dynamic graphics as part of
multidimensional data analysis. Chapters include
clustering, supervised classification, and working
with missing values. A variety of plots and
interaction methods are used in each analysis, often
starting with brushing linked low-dimensional views
and working up to manual manipulation of tours of
several variables. The role of graphical methods is
shown at each step of the analysis, not only in the
early exploratory phase, but in the later stages, too,
when comparing and evaluating models. All examples
are based on freely available software: GGobi for
interactive graphics and R for static graphics,
modeling, and programming. The printed book is
augmented by a wealth of material on the web,
encouraging readers follow the examples themselves.
The web site has all the data and code necessary to
reproduce the analyses in the book, along with movies
demonstrating the examples.},
orderinfo = {springer.txt}
}
@book{R:Siegmund+Yakir:2007,
author = {David Siegmund and Benjamin Yakir},
title = {The Statistics of Gene Mapping},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-49684-9},
publisherurl = {http://www.springer.com/978-0-387-49684-9},
abstract = {This book details the statistical concepts used in
gene mapping, first in the experimental context of
crosses of inbred lines and then in outbred
populations, primarily humans. It presents elementary
principles of probability and statistics, which are
implemented by computational tools based on the R
programming language to simulate genetic experiments
and evaluate statistical analyses. Each chapter
contains exercises, both theoretical and
computational, some routine and others that are more
challenging. The R programming language is developed
in the text.},
orderinfo = {springer.txt}
}
@book{R:Sachs+Hedderich:2006,
author = {Lothar Sachs and J{\"u}rgen Hedderich},
title = {{Angewandte Statistik. Methodensammlung mit R}},
year = 2006,
edition = {12th (completely revised)},
publisher = {Springer},
address = {Berlin, Heidelberg},
note = {ISBN 978-3-540-32160-6},
publisherurl = {http://www.springer.com/978-3-540-32160-6},
abstract = {Die Anwendung statistischer Methoden wird heute in der
Regel durch den Einsatz von Computern unterst{\"u}tzt.
Das Programm R ist dabei ein leicht erlernbares und
flexibel einzusetzendes Werkzeug, mit dem der Prozess
der Datenanalyse nachvollziehbar verstanden und
gestaltet werden kann. Diese 12., vollst{\"a}ndig neu
bearbeitete Auflage veranschaulicht Anwendung und
Nutzen des Programms anhand zahlreicher mit R
durchgerechneter Beispiele. Sie erl{\"a}utert
statistische Ans{\"a}tze und gibt leicht fasslich,
anschaulich und praxisnah Studenten, Dozenten und
Praktikern mit unterschiedlichen Vorkenntnissen die
notwendigen Details, um Daten zu gewinnen, zu
beschreiben und zu beurteilen. Neben Hinweisen zur
Planung und Auswertung von Studien erm{\"o}glichen
viele Beispiele, Querverweise und ein
ausf{\"u}hrliches Sachverzeichnis einen gezielten
Zugang zur Statistik, insbesondere für Mediziner,
Ingenieure und Naturwissenschaftler.},
language = {de}
}
@book{R:Iacus:2007,
author = {Stefano M. Iacus},
title = {Simulation and Inference for Stochastic Differential
Equations: With {R} Examples},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-75838-1},
publisherurl = {http://www.springer.com/978-0-387-75838-1},
abstract = {This book is very different from any other publication
in the field and it is unique because of its focus on
the practical implementation of the simulation and
estimation methods presented. The book should be
useful to practitioners and students with minimal
mathematical background, but because of the many R
programs, probably also to many mathematically well
educated practitioners. Many of the methods presented
in the book have, so far, not been used much in
practice because the lack of an implementation in a
unified framework. This book fills the gap. With the
R code included in this book, a lot of useful methods
become easy to use for practitioners and students. An
R package called `sde' provides functionswith easy
interfaces ready to be used on empirical data from
real life applications. Although it contains a wide
range of results, the book has an introductory
character and necessarily does not cover the whole
spectrum of simulation and inference for general
stochastic differential equations. The book is
organized in four chapters. The first one introduces
the subject and presents several classes of processes
used in many fields of mathematics, computational
biology, finance and the social sciences. The second
chapter is devoted to simulation schemes and covers
new methods not available in other milestones
publication known so far. The third one is focused on
parametric estimation techniques. In particular, it
includes exact likelihood inference, approximated and
pseudo-likelihood methods, estimating functions,
generalized method of moments and other techniques.
The last chapter contains miscellaneous topics like
nonparametric estimation, model identification and
change point estimation. The reader non-expert in R
language, will find a concise introduction to this
environment focused on the subject of the book which
should allow for instant use of the proposed material.
To each R functions presented in the book a
documentation page is available at the end of the
book.},
orderinfo = {springer.txt}
}
@book{R:Rizzo:2008,
author = {Maria L. Rizzo},
title = {Statistical Computing with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2008,
address = {Boca Raton, FL},
note = {ISBN 1-584-88545-9},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C5459},
abstract = {This book covers the traditional core material of
computational statistics, with an emphasis on using
the R language via an examples-based approach.
Suitable for an introductory course in computational
statistics or for self-study, it includes R code for
all examples and R notes to help explain the R
programming concepts.},
orderinfo = {crcpress.txt}
}
@book{R:Greenacre:2007,
author = {Michael Greenacre},
title = {Correspondence Analysis in Practice, Second Edition},
publisher = {Chapman \& Hall/CRC},
year = 2007,
address = {Boca Raton, FL},
note = {ISBN 1-584-88616-1},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6161},
abstract = {This book shows how the versatile method of
correspondence analysis (CA) can be used for data
visualization in a wide variety of situations. T his
completely revised, up-to-date edition features a
didactic approach with self-contained chapters,
extensive marginal notes, informative figure and table
captions, and end-of-chapter summaries. It includes a
computational appendix that provides the R commands
that correspond to most of the analyses featured in
the book.},
orderinfo = {crcpress.txt}
}
@book{R:Gentleman:2008,
author = {Robert Gentleman},
title = {Bioinformatics with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2008,
address = {Boca Raton, FL},
note = {ISBN 1-420-06367-7},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6367},
abstract = {The Bioconductor project was initiated in 2001 to
provide a resource of R packages that specifically
address bioinformatics problems. Written by the
leader of this project and the original developer of
the R software, this book provides an overview of
techniques to develop R programming skills for
bioinformatics. The book presents comprehensive
coverage of a broad range of key topics, including R
language fundamentals, object-oriented programming in
R, foreign language interfaces, building R packages,
handling different data technologies, and debugging.
It includes a number of detailed illustrative
bioinformatics examples as well as exercises to
demonstrate techniques.},
orderinfo = {crcpress.txt}
}
@book{R:Boland:2007,
author = {Philip J. Boland},
title = {Statistical and Probabilistic Methods in Actuarial
Science},
publisher = {Chapman \& Hall/CRC},
year = 2007,
address = {Boca Raton, FL},
note = {ISBN 1-584-88695-1},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6951},
abstract = {This book covers many of the diverse methods in
applied probability and statistics for students
aspiring to careers in insurance, actuarial science,
and finance. It presents an accessible, sound
foundation in both the theory and applications of
actuarial science. It encourages students to use the
statistical software package R to check examples and
solve problems.},
orderinfo = {crcpress.txt}
}
@book{R:Sarkar:2007,
author = {Sarkar, Deepayan},
title = {Lattice Multivariate Data Visualization with {R}},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-75968-5},
publisherurl = {http:///www.springer.com/978-0-387-75968-5},
abstract = {R is rapidly growing in popularity as the environment
of choice for data analysis and graphics both in
academia and industry. Lattice brings the proven
design of Trellis graphics (originally developed for S
by William S. Cleveland and colleagues at Bell Labs)
to R, considerably expanding its capabilities in the
process. Lattice is a powerful and elegant high level
data visualization system that is sufficient for most
everyday graphics needs, yet flexible enough to be
easily extended to handle demands of cutting edge
research. Written by the author of the lattice
system, this book describes it in considerable depth,
beginning with the essentials and systematically
delving into specific low levels details as necessary.
No prior experience with lattice is required to read
the book, although basic familiarity with R is
assumed. The book contains close to 150 figures
produced with lattice. Many of the examples emphasize
principles of good graphical design; almost all use
real data sets that are publicly available in various
R packages. All code and figures in the book are also
available online, along with supplementary material
covering more advanced topics.},
orderinfo = {springer.txt}
}
@book{R:Chambers:2007,
author = {Chambers, John M.},
title = {Software for Data Analysis: Programming with {R}},
publisher = {Springer},
year = 2007,
address = {New York},
note = {ISBN 978-0-387-75935-7},
publisherurl = {http:///www.springer.com/978-0-387-75935-7},
abstract = {John Chambers has been the principal designer of the S
language since its beginning, and in 1999 received the
ACM System Software award for S, the only statistical
software to receive this award. He is author or
coauthor of the landmark books on S. Now he turns to
R, the enormously successful open-source system based
on the S language. R's international support and the
thousands of packages and other contributions have
made it the standard for statistical computing in
research and teaching. This book guides the reader
through programming with R, from interactive use
through all stages from simple functions to the design
of R packages. It includes key modern enhancements
such as classes and methods, namespaces and interfaces
to spreadsheets and data bases.},
orderinfo = {springer.txt}
}
@book{R:Braun+Murdoch:2007,
author = {W. John Braun and Duncan J. Murdoch},
title = {A First Course in Statistical Programming with {R}},
year = 2007,
publisher = {Cambridge University Press},
address = {Cambridge},
note = {ISBN 978-0521872652},
pages = 362,
url = {http://www.stats.uwo.ca/faculty/braun/statprog/},
publisherurl = {http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780521872652},
abstract = {This book introduces students to statistical
programming, using R as a basis. Unlike other
introductory books on the R system, this book
emphasizes programming, including the principles that
apply to most computing languages, and techniques used
to develop more complex projects.}
}
@book{R:Keele:2008,
author = {Keele, Luke},
title = {Semiparametric Regression for the Social Sciences},
publisher = {Wiley},
address = {Chichester, UK},
year = 2008,
note = {ISBN 978-0470319918},
url = {http://www.polisci.ohio-state.edu/faculty/lkeele/keele.html},
publisherurl = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470319917.html},
abstract = {Smoothing methods have been little used within the
social sciences. Semiparametric Regression for the
Social Sciences sets out to address this situation by
providing an accessible introduction to the subject,
filled with examples drawn from the social and
political sciences. Readers are introduced to the
principles of nonparametric smoothing and to a wide
variety of smoothing methods. The author also explains
how smoothing methods can be incorporated into
parametric linear and generalized linear models. The
use of smoothers with these standard statistical
models allows the estimation of more flexible
functional forms whilst retaining the interpretability
of parametric models. The full potential of these
techniques is highlighted via the use of detailed
empirical examples drawn from the social and political
sciences. Each chapter features exercises to aid in
the understanding of the methods and applications.
All examples in the book were estimated in R. The
book contains an appendix with R commands to introduce
readers to estimating these models in R. All the R
code for the examples in the book are available from
the author's website and the publishers website.}
}
@book{R:Claude:2008,
author = {Claude, Julien},
title = {Morphometrics with {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-77789-4},
publisherurl = {http:///www.springer.com/978-0-387-77789-4},
abstract = {Quantifying shape and size variation is essential in
evolutionary biology and in many other disciplines.
Since the ``morphometric revolution of the 90s,'' an
increasing number of publications in applied and
theoretical morphometrics emerged in the new
discipline of statistical shape analysis. The R
language and environment offers a single platform to
perform a multitude of analyses from the acquisition
of data to the production of static and interactive
graphs. This offers an ideal environment to analyze
shape variation and shape change. This open-source
language is accessible for novices and for experienced
users. Adopting R gives the user and developer
several advantages for performing morphometrics:
evolvability, adaptability, interactivity, a single
and comprehensive platform, possibility of interfacing
with other languages and software, custom analyses,
and graphs. The book explains how to use R for
morphometrics and provides a series of examples of
codes and displays covering approaches ranging from
traditional morphometrics to modern statistical shape
analysis such as the analysis of landmark data, Thin
Plate Splines, and Fourier analysis of outlines. The
book fills two gaps: the gap between theoreticians and
students by providing worked examples from the
acquisition of data to analyses and hypothesis
testing, and the gap between user and developers by
providing and explaining codes for performing all the
steps necessary for morphometrics rather than
providing a manual for a given software or package.
Students and scientists interested in shape analysis
can use the book as a reference for performing applied
morphometrics, while prospective researchers will
learn how to implement algorithms or interfacing R for
new methods. In addition, adopting the R philosophy
will enhance exchanges within and outside the
morphometrics community. Julien Claude is
evolutionary biologist and palaeontologist at the
University of Montpellier 2 where he got his Ph.D. in
2003. He works on biodiversity and phenotypic
evolution of a variety of organisms, especially
vertebrates. He teaches evolutionary biology and
biostatistics to undergraduate and graduate students
and has developed several functions in R for the
package APE.},
orderinfo = {springer.txt}
}
@book{R:Pfaff:2008,
author = {Pfaff, Bernhard},
title = {Analysis of Integrated and Cointegrated Time Series
with {R}, Second Edition},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-75966-1},
publisherurl = {http:///www.springer.com/978-0-387-75966-1},
abstract = {The analysis of integrated and co-integrated time
series can be considered as the main methodology
employed in applied econometrics. This book not only
introduces the reader to this topic but enables him to
conduct the various unit root tests and co-integration
methods on his own by utilizing the free statistical
programming environment R. The book encompasses
seasonal unit roots, fractional integration, coping
with structural breaks, and multivariate time series
models. The book is enriched by numerous programming
examples to artificial and real data so that it is
ideally suited as an accompanying text book to
computer lab classes. The second edition adds a
discussion of vector auto-regressive, structural
vector auto-regressive, and structural vector
error-correction models. To analyze the interactions
between the investigated variables, further impulse
response function and forecast error variance
decompositions are introduced as well as forecasting.
The author explains how these model types relate to
each other. Bernhard Pfaff studied economics at the
universities of G{\"o}ttingen, Germany; Davis,
California; and Freiburg im Breisgau, Germany. He
obtained a diploma and a doctorate degree at the
economics department of the latter entity where he was
employed as a research and teaching assistant. He has
worked for many years as economist and quantitative
analyst in research departments of financial
institutions and he is the author and maintainer of
the contributed R packages ``urca'' and ``vars.''},
orderinfo = {springer.txt}
}
@book{R:Spector:2008,
author = {Phil Spector},
title = {Data Manipulation with {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-74730-9},
publisherurl = {http:///www.springer.com/978-0-387-74730-9},
abstract = {Since its inception, R has become one of the
preeminent programs for statistical computing and data
analysis. The ready availability of the program,
along with a wide variety of packages and the
supportive R community make R an excellent choice for
almost any kind of computing task related to
statistics. However, many users, especially those
with experience in other languages, do not take
advantage of the full power of R. Because of the
nature of R, solutions that make sense in other
languages may not be very efficient in R. This book
presents a wide array of methods applicable for
reading data into R, and efficiently manipulating that
data. In addition to the built-in functions, a number
of readily available packages from CRAN (the
Comprehensive R Archive Network) are also covered.
All of the methods presented take advantage of the
core features of R: vectorization, efficient use of
subscripting, and the proper use of the varied
functions in R that are provided for common data
management tasks. Most experienced R users discover
that, especially when working with large data sets, it
may be helpful to use other programs, notably
databases, in conjunction with R. Accordingly, the
use of databases in R is covered in detail, along with
methods for extracting data from spreadsheets and
datasets created by other programs. Character
manipulation, while sometimes overlooked within R, is
also covered in detail, allowing problems that are
traditionally solved by scripting languages to be
carried out entirely within R. For users with
experience in other languages, guidelines for the
effective use of programming constructs like loops are
provided. Since many statistical modeling and
graphics functions need their data presented in a data
frame, techniques for converting the output of
commonly used functions to data frames are provided
throughout the book. Using a variety of examples
based on data sets included with R, along with easily
simulated data sets, the book is recommended to anyone
using R who wishes to advance from simple examples to
practical real-life data manipulation solutions.},
orderinfo = {springer.txt}
}
@book{R:Cryer+Chan:2008,
author = {Jonathan D. Cryer and Kung-Sik Chan},
title = {Time Series Analysis With Applications in {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-75958-6},
publisherurl = {http:///www.springer.com/978-0-387-75958-6},
abstract = {Time Series Analysis With Applications in R, Second
Edition, presents an accessible approach to
understanding time series models and their
applications. Although the emphasis is on time domain
ARIMA models and their analysis, the new edition
devotes two chapters to the frequency domain and three
to time series regression models, models for
heteroscedasticty, and threshold models. All of the
ideas and methods are illustrated with both real and
simulated data sets. A unique feature of this edition
is its integration with the R computing environment.
The tables and graphical displays are accompanied by
the R commands used to produce them. An extensive R
package, TSA, which contains many new or revised R
functions and all of the data used in the book,
accompanies the written text. Script files of R
commands for each chapter are available for download.
There is also an extensive appendix in the book that
leads the reader through the use of R commands and the
new R package to carry out the analyses.},
orderinfo = {springer.txt}
}
@book{R:Shumway+Stoffer:2006,
author = {Robert H. Shumway and David S. Stoffer},
title = {Time Series Analysis and Its Applications With {R}
Examples},
publisher = {Springer},
year = 2006,
address = {New York},
note = {ISBN 978-0-387-29317-2},
publisherurl = {http:///www.springer.com/978-0-387-29317-2},
abstract = {Time Series Analysis and Its Applications presents a
balanced and comprehensive treatment of both time and
frequency domain methods with accompanying theory.
Numerous examples using non-trivial data illustrate
solutions to problems such as evaluating pain
perception experiments using magnetic resonance
imaging or monitoring a nuclear test ban treaty. The
book is designed to be useful as a text for graduate
level students in the physical, biological and social
sciences and as a graduate level text in statistics.
Some parts may also serve as an undergraduate
introductory course. Theory and methodology are
separated to allow presentations on different levels.
Material from the earlier 1988 Prentice-Hall text
Applied Statistical Time Series Analysis has been
updated by adding modern developments involving
categorical time sries analysis and the spectral
envelope, multivariate spectral methods, long memory
series, nonlinear models, longitudinal data analysis,
resampling techniques, ARCH models, stochastic
volatility, wavelets and Monte Carlo Markov chain
integration methods. These add to a classical
coverage of time series regression, univariate and
multivariate ARIMA models, spectral analysis and
state-space models. The book is complemented by
ofering accessibility, via the World Wide Web, to the
data and an exploratory time series analysis program
ASTSA for Windows that can be downloaded as Freeware.},
orderinfo = {springer.txt}
}
@book{R:Peng+Dominici:2008,
author = {Roger D. Peng and Francesca Dominici},
title = { Statistical Methods for Environmental Epidemiology
with {R}: A Case Study in Air Pollution and Health },
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-78166-2},
publisherurl = {http:///www.springer.com/978-0-387-78166-2},
abstract = {Advances in statistical methodology and computing have
played an important role in allowing researchers to
more accurately assess the health effects of ambient
air pollution. The methods and software developed in
this area are applicable to a wide array of problems
in environmental epidemiology. This book provides an
overview of the methods used for investigating the
health effects of air pollution and gives examples and
case studies in R which demonstrate the application of
those methods to real data. The book will be useful
to statisticians, epidemiologists, and graduate
students working in the area of air pollution and
health and others analyzing similar data. The authors
describe the different existing approaches to
statistical modeling and cover basic aspects of
analyzing and understanding air pollution and health
data. The case studies in each chapter demonstrate
how to use R to apply and interpret different
statistical models and to explore the effects of
potential confounding factors. A working knowledge of
R and regression modeling is assumed. In-depth
knowledge of R programming is not required to
understand and run the examples. Researchers in this
area will find the book useful as a ``live''
reference. Software for all of the analyses in the
book is downloadable from the web and is available
under a Free Software license. The reader is free to
run the examples in the book and modify the code to
suit their needs. In addition to providing the
software for developing the statistical models, the
authors provide the entire database from the National
Morbidity, Mortality, and Air Pollution Study (NMMAPS)
in a convenient R package. With the database, readers
can run the examples and experiment with their own
methods and ideas.},
orderinfo = {springer.txt}
}
@book{R:Bivand+Pebesma+Gomez-Rubio:2008,
author = {Roger S. Bivand and Edzer J. Pebesma and Virgilio
G{\'o}mez-Rubio},
title = {Applied Spatial Data Analysis with {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-78170-},
publisherurl = {http:///www.springer.com/978-0-387-78170-9},
abstract = {Applied Spatial Data Analysis with R is divided into
two basic parts, the first presenting R packages,
functions, classes and methods for handling spatial
data. This part is of interest to users who need to
access and visualise spatial data. Data import and
export for many file formats for spatial data are
covered in detail, as is the interface between R and
the open source GRASS GIS. The second part showcases
more specialised kinds of spatial data analysis,
including spatial point pattern analysis,
interpolation and geostatistics, areal data analysis
and disease mapping. The coverage of methods of
spatial data analysis ranges from standard techniques
to new developments, and the examples used are largely
taken from the spatial statistics literature. All the
examples can be run using R contributed packages
available from the CRAN website, with code and
additional data sets from the book's own website.
This book will be of interest to researchers who
intend to use R to handle, visualise, and analyse
spatial data. It will also be of interest to spatial
data analysts who do not use R, but who are interested
in practical aspects of implementing software for
spatial data analysis. It is a suitable companion
book for introductory spatial statistics courses and
for applied methods courses in a wide range of
subjects using spatial data, including human and
physical geography, geographical information systems,
the environmental sciences, ecology, public health and
disease control, economics, public administration and
political science. The book has a website where
coloured figures, complete code examples, data sets,
and other support material may be found:
\url{http://www.asdar-book.org}.},
orderinfo = {springer.txt}
}
@book{R:Nason:2008,
author = {G. P. Nason},
title = {Wavelet Methods in Statistics with {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-75960-9},
publisherurl = {http:///www.springer.com/978-0-387-75960-9},
abstract = {Wavelet methods have recently undergone a rapid period
of development with important implications for a
number of disciplines including statistics. This book
fulfils three purposes. First, it is a gentle
introduction to wavelets and their uses in statistics.
Second, it acts as a quick and broad reference to many
recent developments in the area. The book
concentrates on describing the essential elements and
provides comprehensive source material references.
Third, the book intersperses R code that explains and
demonstrates both wavelet and statistical methods.
The code permits the user to learn the methods, to
carry out their own analyses and further develop their
own methods. The book is designed to be read in
conjunction with WaveThresh4, the freeware R package
for wavelets. The book introduces the wavelet
transform by starting with the simple Haar wavelet
transform and then builds to consider more general
wavelets such as the Daubechies compactly supported
series. The book then describes the evolution of
wavelets in the directions of complex-valued wavelets,
non-decimated transforms, multiple wavelets and
wavelet packets as well as giving consideration to
boundary conditions initialization. Later chapters
explain the role of wavelets in nonparametric
regression problems via a variety of techniques
including thresholding, cross-validation, SURE,
false-discovery rate and recent Bayesian methods, and
also consider how to deal with correlated and
non-Gaussian noise structures. The book also looks at
how nondecimated and packet transforms can improve
performance. The penultimate chapter considers the
role of wavelets in both stationary and non-stationary
time series analysis. The final chapter describes
recent work concerning the role of wavelets for
variance stabilization for non-Gaussian intensity
estimation. The book is aimed at final year
undergraduate and Masters students in a numerate
discipline (such as mathematics, statistics, physics,
economics and engineering) and would also suit as a
quick reference for postgraduate or research level
activity. The book would be ideal for a researcher to
learn about wavelets, to learn how to use wavelet
software and then to adapt the ideas for their own
purposes.},
orderinfo = {springer.txt}
}
@book{R:Kleiber+Zeileis:2008,
author = {Christian Kleiber and Achim Zeileis},
title = {Applied Econometrics with R},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-77316-2},
publisherurl = {http:///www.springer.com/978-0-387-77316-2},
abstract = {This is the first book on applied econometrics using
the R system for statistical computing and graphics.
It presents hands-on examples for a wide range of
econometric models, from classical linear regression
models for cross-section, time series or panel data
and the common non-linear models of microeconometrics
such as logit, probit and tobit models, to recent
semiparametric extensions. In addition, it provides a
chapter on programming, including simulations,
optimization, and an introduction to R tools enabling
reproducible econometric research. An R package
accompanying this book, AER, is available from the
Comprehensive R Archive Network (CRAN) at
\url{http://CRAN.R-project.org/package=AER}. It
contains some 100 data sets taken from a wide variety
of sources, the full source code for all examples used
in the text plus further worked examples, e.g., from
popular textbooks. The data sets are suitable for
illustrating, among other things, the fitting of wage
equations, growth regressions, hedonic regressions,
dynamic regressions and time series models as well as
models of labor force participation or the demand for
health care. The goal of this book is to provide a
guide to R for users with a background in economics or
the social sciences. Readers are assumed to have a
background in basic statistics and econometrics at the
undergraduate level. A large number of examples should
make the book of interest to graduate students,
researchers and practitioners alike. },
orderinfo = {springer.txt}
}
@book{R:Reimann+Filzmoser+Garrett:2008,
author = {Clemens Reimann and Peter Filzmoser and Robert Garrett
and Rudolf Dutter},
title = {Statistical Data Analysis Explained: Applied
Environmental Statistics with {R}},
publisher = {Wiley},
address = {Chichester, UK},
year = 2008,
note = {ISBN: 978-0-470-98581-6},
url = {http://www.statistik.tuwien.ac.at/StatDA},
publisherurl = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-047098581X.html},
abstract = {Few books on statistical data analysis in the natural
sciences are written at a level that a
non-statistician will easily understand. This is a
book written in colloquial language, avoiding
mathematical formulae as much as possible, trying to
explain statistical methods using examples and
graphics instead. To use the book efficiently, readers
should have some computer experience. The book starts
with the simplest of statistical concepts and carries
readers forward to a deeper and more extensive
understanding of the use of statistics in
environmental sciences. The book concerns the
application of statistical and other computer methods
to the management, analysis and display of spatial
data. These data are characterised by including
locations (geographic coordinates), which leads to the
necessity of using maps to display the data and the
results of the statistical methods. Although the book
uses examples from applied geochemistry, and a large
geochemical survey in particular, the principles and
ideas equally well apply to other natural sciences,
e.g., environmental sciences, pedology, hydrology,
geography, forestry, ecology, and health
sciences/epidemiology. The book is unique because it
supplies direct access to software solutions (based on
R, the Open Source version of the S-language for
statistics) for applied environmental statistics. For
all graphics and tables presented in the book, the
R-scripts are provided in the form of executable
R-scripts. In addition, a graphical user interface
for R, called DAS+R, was developed for convenient,
fast and interactive data analysis. Statistical Data
Analysis Explained: Applied Environmental Statistics
with R provides, on an accompanying website, the
software to undertake all the procedures discussed,
and the data employed for their description in the
book.}
}
@book{R:Sheather:2008,
author = {Simon Sheather},
title = {A Modern Approach to Regression with {R}},
publisher = {Springer},
year = 2008,
address = {New York},
note = {ISBN 978-0-387-09607-0},
publisherurl = {http:///www.springer.com/978-0-387-09607-0},
abstract = {A Modern Approach to Regression with R focuses on
tools and techniques for building regression models
using real-world data and assessing their
validity. When weaknesses in the model are identified,
the next step is to address each of these
weaknesses. A key theme throughout the book is that it
makes sense to base inferences or conclusions only on
valid models. The regression output and plots that
appear throughout the book have been generated using
R. On the book website you will find the R code used
in each example in the text. You will also find
SAS code and STATA code to produce the equivalent
output on the book website. Primers containing
expanded explanations of R, SAS and STATA and their
use in this book are also available on the book
website. The book contains a number of new real data
sets from applications ranging from rating
restaurants, rating wines, predicting newspaper
circulation and magazine revenue, comparing the
performance of NFL kickers, and comparing finalists in
the Miss America pageant across states. One of the
aspects of the book that sets it apart from many other
regression books is that complete details are provided
for each example. The book is aimed at first year
graduate students in statistics and could also be used
for a senior undergraduate class.},
orderinfo = {springer.txt}
}
@comment{{-----------------end-of-books------------------------------------}}
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