A Handbook of Statistical Analyses Using S-PLUS, 

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
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Цена: 55890.00 T
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Описание: An Introduction to Statistical Learning 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, deep learning, survival analysis, multiple testing, 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. 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.

An Introduction to Statistical Learning 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.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Автор: Christian Robert; George Casella
Название: Monte Carlo Statistical Methods
ISBN: 1441919392 ISBN-13(EAN): 9781441919397
Издательство: Springer
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Цена: 111790.00 T
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Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Автор: Chandrupatla, Tirupathi, Belegundu, Ashok
Название: Introduction to finite elements in engineering
ISBN: 1108841414 ISBN-13(EAN): 9781108841412
Издательство: Cambridge Academ
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Цена: 79190.00 T
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Описание: Thoroughly updated with improved pedagogy, the fifth edition provides senior undergraduate and graduate students with a clear, comprehensive introduction to the field. Features enhanced coverage of introductory topics, over thirty additional solved problems; downloadable Matlab, Python, C, and Javascript code; and solutions for instructors.

Автор: Mehmetoglu, Mehmet Mittner, Matthias
Название: Applied statistics using r
ISBN: 1526476223 ISBN-13(EAN): 9781526476227
Издательство: Sage Publications
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Цена: 49620.00 T
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Описание: Drawing on real world data to showcase different techniques, this practical book helps you use R for data analysis in your own research.

Название: Handbook of bayesian variable selection
ISBN: 0367543788 ISBN-13(EAN): 9780367543785
Издательство: Taylor&Francis
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Цена: 61240.00 T
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Автор: Cui, Xinping ; Dickhaus, Thorsten ; Ding, Ying
Название: Handbook of Multiple Comparisons
ISBN: 1032111550 ISBN-13(EAN): 9781032111551
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Автор: Nisbet, Robert
Название: Handbook of Statistical Analysis
ISBN: 0443158452 ISBN-13(EAN): 9780443158452
Издательство: Elsevier Science
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Цена: 98760.00 T
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Автор: Li Chunyan
Название: Time Series Data Analysis in Oceanography: Applications Using MATLAB
ISBN: 1108474276 ISBN-13(EAN): 9781108474276
Издательство: Cambridge University Press
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Цена: 83380.00 T
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Описание: Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other Earth science subjects, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB.

Автор: West, Brady T. (university Of Michigan, Ann Arbor, Usa) Welch, Kathleen B. (university Of Michigan, Ann Arbor, Usa) Galecki, Andrzej T (university Of
Название: Linear mixed models
ISBN: 1032019328 ISBN-13(EAN): 9781032019321
Издательство: Taylor&Francis
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Цена: 91860.00 T
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Описание: The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. There is a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included

Название: Understanding Statistical Concepts Using S-plus
ISBN: 0805836233 ISBN-13(EAN): 9780805836233
Издательство: Taylor&Francis
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Цена: 47970.00 T
Наличие на складе: Нет в наличии.

Автор: Tadesse Mahlet G., Vannucci Marina
Название: Handbook of Bayesian Variable Selection
ISBN: 0367543761 ISBN-13(EAN): 9780367543761
Издательство: Taylor&Francis
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Цена: 163330.00 T
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Описание: The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions.

Автор: Lu
Название: Handbook of Statistical Bioinformatics
ISBN: 3662659018 ISBN-13(EAN): 9783662659014
Издательство: Springer
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Цена: 186330.00 T
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Описание: Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.