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Introduction to General and Generalized Linear Models

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IntroductionExamples of types of data Motivating examples A first view on the modelsThe Likelihood PrincipleIntroduction Point estimation theory The likelihood function The score function The information matrix Alternative parameterizations of the likelihood The maximum likelihood estimate (MLE) Distribution of the ML estimator Generalized loss-function and deviance Quadratic approximation of the log-likelihood Read more...

Abstract: IntroductionExamples of types of data Motivating examples A first view on the modelsThe Likelihood PrincipleIntroduction Point estimation theory The likelihood function The score function The information matrix Alternative parameterizations of the likelihood The maximum likelihood estimate (MLE) Distribution of the ML estimator Generalized loss-function and deviance Quadratic approximation of the log-likelihood Likelihood ratio tests Successive testing in hypothesis chains Dealing with nuisance parameters General Linear ModelsIntroduction The multivariate normal distribution General linear mod

Author(s): Madsen, Henrik; Thyregod, Poul
Series: Chapman & Hall/CRC Texts in Statistical Science
Publisher: CRC Press
Year: 2010

Language: English
Pages: 307
City: Hoboken
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;

Content: Cover
Title
Copyright
Contents
Preface
CHAPTER 1: Introduction
CHAPTER 2: The likelihood principle
CHAPTER 3: General linear models
CHAPTER 4: Generalized linear models
CHAPTER 5: Mixed effects models
CHAPTER 6: Hierarchical models
CHAPTER 7: Real life inspired problems
APPENDIX A: Supplement on the law of error propagation
APPENDIX B: Some probability distributions
APPENDIX C: List of symbols
Bibliography