Linear Models

by Shayle R. Searle

Overview

This 1971 classic on linear models is once again available—as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

Product Details

ISBN-13:	9781118491768
Publisher:	Wiley
Publication date:	09/04/2012
Series:	Wiley Series in Probability and Statistics - Applied Probability and Statistics Section
Sold by:	JOHN WILEY & SONS
Format:	eBook
Pages:	560
File size:	9 MB

About the Author

Shayle R. Searle, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of Linear Models, Linear Models for Unbalanced Data, Matrix Algebra Useful for Statistics, and Variance Components, all published by Wiley.

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Table of Contents

Generalized Inverse Matrices.
Distributions and Quadratic Forms.
Regression, or the Full Rank Model.
Introducing Linear Models: Regression on Dummy Variables.
Models Not of Full Rank.
Two Elementary Models.
The 2-Way Crossed Classification.
Some Other Analyses.
Introduction to Variance Components.
Methods of Estimating Variance Components from Unbalanced Data.
Variance Component Estimation from Unbalanced Data: Formulae.
Literature Cited.
Statistical Tables.
Index.

Editorial Reviews

Designed to assist those whose data cannot satisfy the strictures of carefully managed and well-designed experiments, this classic describes general procedures of estimation and hypothesis testing for linear statistical models and shows their application for unbalanced data (i.e. unequal-subclass-numbers data) to certain specific models that often arise in research and survey work. In addition, three chapters are devoted to methods and results for estimating variance components, particularly for unbalanced data. Annotation c. by Book News, Inc., Portland, Or.

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