# Introduction To General And Generalized Linear Models (chapman & Hall/crc Texts In Statistical Science)

by Henrik Madsen /
2010 / English / PDF

4.1 MB Download

Bridging the gap between theory and practice for modern
statistical model building,

Bridging the gap between theory and practice for modern
statistical model building,Introduction to General and
Generalized Linear Models

Introduction to General and
Generalized Linear Models presents likelihood-based
techniques for statistical modelling using various types of data.
Implementations using R are provided throughout the text,
although other software packages are also discussed. Numerous
examples show how the problems are solved with R.

presents likelihood-based
techniques for statistical modelling using various types of data.
Implementations using R are provided throughout the text,
although other software packages are also discussed. Numerous
examples show how the problems are solved with R.
After describing the necessary likelihood theory, the book covers
both general and generalized linear models using the same
likelihood-based methods. It presents the corresponding/parallel
results for the general linear models first, since they are
easier to understand and often more well known. The authors then
explore random effects and mixed effects in a Gaussian context.
They also introduce non-Gaussian hierarchical models that are
members of the exponential family of distributions. Each chapter
contains examples and guidelines for solving the problems via R.

After describing the necessary likelihood theory, the book covers
both general and generalized linear models using the same
likelihood-based methods. It presents the corresponding/parallel
results for the general linear models first, since they are
easier to understand and often more well known. The authors then
explore random effects and mixed effects in a Gaussian context.
They also introduce non-Gaussian hierarchical models that are
members of the exponential family of distributions. Each chapter
contains examples and guidelines for solving the problems via R.
Providing a flexible framework for data analysis and model
building, this text focuses on the statistical methods and models
that can help predict the expected value of an outcome,
dependent, or response variable. It offers a sound introduction
to general and generalized linear models using the popular and
powerful likelihood techniques. Ancillary materials are available
at www.imm.dtu.dk/~hm/GLM

Providing a flexible framework for data analysis and model
building, this text focuses on the statistical methods and models
that can help predict the expected value of an outcome,
dependent, or response variable. It offers a sound introduction
to general and generalized linear models using the popular and
powerful likelihood techniques. Ancillary materials are available
at www.imm.dtu.dk/~hm/GLM