Extending Linear Model With R.
Publisher: Chapman & Hall/CRC
Published: 2004
Place of publication: London
Language: eng
Format: [electronic resource]
Number of pages: 345
ISBN-13: 9780203492284
Subjects: Analysis of variance
Description: Linear models are central to the practice of statistics. They are part of the core knowledgeexpected of any applied statistician. Linear models are the foundation of a broad range ofstatistical methodologies; this book is a survey of techniques that grow from a linearmodel. Our starting point is the regression model with response y and predictors x1, ... xp. The model takes the form:y=a 0+a 1x1+ ... +a pxp+a where a  is normally distributed. This book presents three extensions to this framework. The first generalizes the y part; the second, the a  part; and the third, the x part of the linearmodel.
Notes: Description based on print version record.
Table of contents: title: Book cover; Series-Title; Title; Copyright; Preface; Contents; 1 Introduction; 2 Binomial Data; 3 Count Regression; 4 Contingency Tables; 5 Multinomial Data; 6 Generalized Linear Models; 7 Other GLMs; 8 Random Effects; 9 Repeated Measures and Longitudinal Data; 10 Mixed Effect Models for Nonnormal Responses; 11 Nonparametric Regression; 12 Additive Models; 13 Trees; 14 Neural Networks; Appendix A; Appendix B; Bibliography; Index.