Linear Models
Least Squares and Alternatives
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Beschreibung
An up-to-date account of the theory and applications of linear models, for use as a textbook in statistics at graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights include: - a special emphasis on sensitivity analysis and model selection; - a chapter devoted to the analysis of categorical data based on logic, loglinear, and logistic regression models; - a chapter devoted to incomplete data sets; - an extensive appendix on matrix theory; - a chapter devoted to the analysis of categorical data based on a unified presentation of generalized linear models including GEE-methods for correlated response; - a chapter devoted to incomplete data sets including regression diagnostics to identify Non-MCAR-processes The material covered is thus invaluable not only to graduates, but also to researchers and consultants in statistics.
This book is an essential text for graduate statsitics courses and courses where linear models play a part.
This text presents an up-to-date account of the theory and applications of linear models. This book will be an invaluable reference for researchers and graduate students in statistics.
Autor*in
Calyampudi R. Rao
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Fitting Generalized linear model Linear Models Logistic Regression linear regression
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From a review:
L'ENSEIGNEMENT MATHEMATIQUE
"This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses instatistics at the graduate level as well as an accompanying text for other courses in which linear models play a part."
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