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Lavoisier Bokseller: New books in mathematics
2017-09-01T12:00:00+01:00
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Books in mathematics just arrived on Lavoisier Bookseller
© 2018 Lavoisier Bokseller
http://www.lavoisier.eu/books/mathematics/analysis-of-repeated-measures-data/islam/description_3642170
Analysis of Repeated Measures Data
2017-09-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1317040443.jpg" alt="Book's cover:Analysis of Repeated Measures Data" /><br />This book presents a broad range of statistical techniques to address
emerging needs in the field of repeated measures. It also provides a
comprehensive overview of extensions of generalized linear models for the
bivariate exponential family of distributions, which represent a new
development in analysing repeated measures data. The demand for
statistical models for correlated outcomes has grown rapidly recently,
mainly due to presence of two types of underlying associations:
associations between outcomes, and associations between explanatory
variables and outcomes.<br><br>The book systematically addresses key
problems arising in the modelling of repeated measures data, bearing in
mind those factors that play a major role in estimating the underlying
relationships between covariates and outcome variables for correlated
outcome data. In addition, it presents new approaches to addressing
current challenges in the field of repeated measures and models based on
conditional and joint probabilities. Markov models of first and higher
orders are used for conditional models in addition to conditional
probabilities as a function of covariates. Similarly, joint models are
developed using both marginal-conditional probabilities as well as joint
probabilities as a function of covariates. <br><br>In addition to
generalized linear models for bivariate outcomes, it highlights extended
semi-parametric models for continuous failure time data and their
applications in order to include models for a broader range of outcome
variables that researchers encounter in various fields. The book further
discusses the problem of analysing repeated measures data for failure time
in the competing risk framework, which is now taking on an increasingly
important role in the field of survival analysis, reliability and
actuarial science. Details on how to perform the analyses are included in
each chapter and supplemented with newly developed R packages and
functions along with SAS codes and macro/IML. It is a valuable resource
for researchers, graduate students and other users of statistical
techniques for analysing repeated measures data.
http://www.lavoisier.eu/books/other/an-introduction-to-statistical-learning/description_2831897
An Introduction to Statistical Learning with Applications in R
2017-09-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1316307853.jpg" alt="Book's cover:An Introduction to Statistical Learning with Applications in R" /><br /><p>
<b>An Introduction to Statistical Learning</b> provides an accessible
overview of the field of statistical learning, an essential toolset for
making sense of the vast and complex data sets that have emerged in
fields ranging from biology to finance to marketing to astrophysics in
the past twenty years. This book presents some of the most important
modeling and prediction techniques, along with relevant applications.
Topics include linear regression, classification, resampling methods,
shrinkage approaches, tree-based methods, support vector machines,
clustering, and more. Color graphics and real-world examples are used to
illustrate the methods presented. Since the goal of this textbook is to
facilitate the use of these statistical learning techniques by
practitioners in science, industry, and other fields, each chapter
contains a tutorial on implementing the analyses and methods presented
in R, an extremely popular open source statistical software platform.
</p>
<p>
Two of the authors co-wrote The Elements of Statistical Learning
(Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference
book for statistics and machine learning researchers. <b>An Introduction
to Statistical Learning</b> covers many of the same topics, but at a
level accessible to a much broader audience. This book is targeted at
statisticians and non-statisticians alike who wish to use cutting-edge
statistical learning techniques to analyze their data. The text assumes
only a previous course in linear regression and no knowledge of matrix
algebra.
</p>
http://www.lavoisier.eu/books/mathematics/clinical-trial-optimization-using-r/dmitrienko/description_3644382
Clinical Trial Optimization Using R
2017-06-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1317042818.jpg" alt="Book's cover:Clinical Trial Optimization Using R" /><br />Clinical Trial Optimization Using R explores a unified and broadly
applicable framework for optimizing decision making and strategy selection
in clinical development, through a series of examples and case studies. It
provides the clinical researcher with a powerful evaluation paradigm, as
well as supportive R tools, to evaluate and select among simultaneous
competing designs or analysis options. It is applicable broadly to
statisticians and other quantitative clinical trialists, who have an
interest in optimizing clinical trials, clinical trial programs, or
associated analytics and decision making.<br><br>This book presents in
depth the Clinical Scenario Evaluation (CSE) framework, and discusses
optimization strategies, including the quantitative assessment of
tradeoffs. A variety of common development challenges are evaluated as
case studies, and used to show how this framework both simplifies and
optimizes strategy selection. Specific settings include optimizing
adaptive designs, multiplicity and subgroup analysis strategies, and
overall development decision-making criteria around Go/No-Go. After this
book, the reader will be equipped to extend the CSE framework to their
particular development challenges as well.
http://www.lavoisier.eu/books/mathematics/generalized-additive-models-an-introduction-with-r-2nd-ed/wood/description_3649818
Generalized Additive Models: An Introduction with R
2017-06-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1317048549.jpg" alt="Book's cover:Generalized Additive Models: An Introduction with R " /><br />The first edition of this book has established itself as one of the
leading references on generalized additive models (GAMs), and the only
book on the topic to be introductory in nature with a wealth of practical
examples and software implementation. It is self-contained, providing the
necessary background in linear models, linear mixed models, and
generalized linear models (GLMs), before presenting a balanced treatment
of the theory and applications of GAMs and related models.<br><br>The
author bases his approach on a framework of penalized regression splines,
and while firmly focused on the practical aspects of GAMs, discussions
include fairly full explanations of the theory underlying the methods. Use
of R software helps explain the theory and illustrates the practical
application of the methodology. Each chapter contains an extensive set of
exercises, with solutions in an appendix or in the book’s R data package
gamair, to enable use as a course text or for self-study.