Foundations of Linear and Generalized Linear Models

Foundations of Linear and Generalized Linear Models

2015 | ISBN: 1118730038 | English | 472 Pages | PDF | 7 MB + 14 MB

A valuable overview of the most important ideas and results in statistical modeling

Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding.

The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features:

An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods
An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems
Numerous examples that use R software for all text data analyses
More than 400 exercises for readers to practice and extend the theory, methods, and data analysis
A supplementary website with datasets for the examples and exercises
An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Download:

http://longfiles.com/44q9t6r5e93k/Foundations_of_Linear_and_Generalized_Linear_Models.pdf.html

[Fast Download] Foundations of Linear and Generalized Linear Models


Ebooks related to "Foundations of Linear and Generalized Linear Models" :
Empirical Research in Statistics Education
TTC - Understanding Calculus: Problems, Solutions, and Tips [repost]
A Companion to Interdisciplinary Stem Project-Based Learning, Second Edition
Intelligent Mathematics II: Applied Mathematics and Approximation Theory
Advances and Applications in Chaotic Systems
High-Precision Methods in Eigenvalue Problems and Their Applications
Mathematics and Technology
Graph Theory
Cornelia Pester - A posteriori error estimation for non-linear eigenvalue problems
Nonlinear Ordinary Differential Equations: Problems and Solutions
Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.