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Lavoisier Bokseller: New books in mathematics
2017-10-01T12:00:00+01:00
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Books in mathematics just arrived on Lavoisier Bookseller
© 2017 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/quantum-field-theory-and-condensed-matter/shankar/description_3655643
Quantum Field Theory and Condensed Matter
2017-08-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1317054631.jpg" alt="Book's cover:Quantum Field Theory and Condensed Matter" /><br />
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/introduction-to-geometric-computing/ghali/description_1864303
Introduction to geometric computing
2017-10-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1865606.jpg" alt="Book's cover:Introduction to geometric computing" /><br />The geometric ideas in computer science, mathematics, engineering, and physics have considerable overlap and students in each of these disciplines will eventually encounter geometric computing problems. The topic is traditionally taught in mathematics departments via geometry courses, and in computer science through computer graphics modules. This text isolates the fundamental topics affecting these disciplines and lies at the intersection of classical geometry and modern computing. The main theme of the book is the definition of coordinate-free geometric software layers for Euclidean, spherical, projective, and oriented-projective geometries. Results are derived from elementary linear algebra and many classical computer graphics problems (including the graphics pipeline) are recast in this new language. Also included is a novel treatment of classical geometric and solid modeling problems. The definition of geometric software layers promotes reuse, speeds up debugging, and prepares the ground for a thorough discussion of advanced topics. Start-up programs are provided for many programming exercises making this an invaluable book for computer science lecturers as well as software developers and researchers in the computer graphics industry.
http://www.lavoisier.eu/books/mathematics/intuitive-probability-et-random-processes-using-matlab/kay/description_1270881
Intuitive probability & random processes using MATLAB
2017-08-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1271655.jpg" alt="Book's cover:Intuitive probability & random processes using MATLAB" /><br />Intuitive Probability and Random Processes using MATLAB® is an introduction to probability and random processes that merges theory with practice. Based on the author's belief that only "hands-on" experience with the material can promote intuitive understanding, the approach is to motivate the need for theory using MATLAB examples, followed by theory and analysis, and finally descriptions of "real-world" examples to acquaint the reader with a wide variety of applications. The latter is intended to answer the usual question "Why do we have to study this?" Other salient features are: heavy reliance on computer simulation for illustration and student exercises, the incorporation of MATLAB programs and code segments, discussion of discrete random variables followed by continuous random variables to minimize confusion, summary sections at the beginning of each chapter, in-line equation explanations, warnings on common errors and pitfalls, over 750 problems designed to help the reader assimilate and extend the concepts. This volume is intended for undergraduate and first-year graduate students in engineering. The practicing engineer as well as others having the appropriate mathematical background will also benefit from this book.
http://www.lavoisier.eu/books/mathematics/elements-of-nonlinear-time-series-analysis-and-forecasting/de/description_3627447
Elements of Nonlinear Time Series Analysis and Forecasting
2017-04-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1317024456.jpg" alt="Book's cover:Elements of Nonlinear Time Series Analysis and Forecasting" /><br /><p>
This book provides an overview of the current state-of-the-art of
nonlinear time series analysis, richly illustrated with examples,
pseudocode algorithms and real-world applications. Avoiding
a “theorem-proof” format, it shows concrete applications on a variety of
empirical time series. The book can be used in graduate courses in
nonlinear time series and at the same time also includes interesting
material for more advanced readers. Though it is largely self-contained,
readers require an understanding of basic linear time series concepts,
Markov chains and Monte Carlo simulation methods.
</p>
<p>
The book covers time-domain and frequency-domain methods for the
analysis of both univariate and multivariate (vector) time series. It
makes a clear distinction between parametric models on the one hand, and
semi- and nonparametric models/methods on the other. This offers the
reader the option of concentrating exclusively on one of these nonlinear
time series analysis methods.
</p>
<p>
To make the book as user friendly as possible, major supporting concepts
and specialized tables are appended at the end of every chapter. In
addition, each chapter concludes with a set of key terms and concepts,
as well as a summary of the main findings. Lastly, the book offers
numerous theoretical and empirical exercises, with answers provided by
the author in an extensive solutions manual.
</p>
http://www.lavoisier.eu/books/mathematics/introduction-to-computational-chemistry-3rd-ed/jensen/description_3599469
Introduction to Computational Chemistry (3rd Ed.)
2017-02-01T12:00:00+01:00
<img src="https://images.lavoisier.net/vignettes/1316991570.jpg" alt="Book's cover:Introduction to Computational Chemistry (3rd Ed.) " /><br /><i>Introduction to Computational Chemistry - 3rd Edition </i>provides a
comprehensive account of the fundamental principles underlying different
computational methods. Fully revised and updated throughout to reflect
important method developments and improvements since publication of the
previous edition, this timely update includes the following significant
revisions and new topics:<br><br>- Polarizable force fields<br>-
Tight-binding DFT<br>- More extensive DFT functionals, excited states and
time dependent molecular properties<br>- Accelerated Molecular Dynamics
methods<br>- Tensor decomposition methods<br>- Cluster analysis<br>-
Reduced scaling and reduced prefactor methods