A Primer on Linear Models

by John F. Monahan

Overview

A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact, finite sample theory supporting common statistical methods.

Product Details

ISBN-13:	9781040079218
Publisher:	CRC Press
Publication date:	03/31/2008
Series:	Chapman & Hall/CRC Texts in Statistical Science
Sold by:	Barnes & Noble
Format:	eBook
Pages:	304
File size:	1 MB

About the Author

John F. Monahan

Table of Contents

Preface. Examples of the General Linear Model. The Linear Least Squares Problem. Estimability and Least Squares Estimators. Gauss-Markov Model. Distributional Theory. Statistical Inference. Further Topics in Testing. Variance Components and Mixed Models. The Multivariate Linear Model. Appendices. Bibliography.

Editorial Reviews

"… I found the book very helpful. … the result is very nice, very readable, and in particular I like the idea of avoiding leaps in the development and proofs, or referring to other sources for the details of the proofs. This is a useful well-written instructive book."
—International Statistical Review

"This work provides a brief, and also complete, foundation for the theory of basic linear models . . . can be used for graduate courses on linear models."
– Nicoleta Breaz, Zentralblatt Math

". . . well written . . . would serve well as the textbook for an introductory course in linear models, or as references for researchers who would like to review the theory of linear models."
– Justine Shults, Department of Biostatistics, University of Pennsylvania School of Medicine, Journal of Biopharmaceutical Statistics

From the Publisher