A Primer on Linear Models
1st Edition
Published on 31. March 2008
304 pages
978-1-040-07921-8 (ISBN)
Description
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.
Reviews / Votes
"... 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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
ISBN-13
978-1-040-07921-8 (9781040079218)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
John F. Monahan
A Primer on Linear Models
Book
08/2017
1st Edition
CRC Press
€282.53
Shipment within 10-20 days
John F. Monahan
A Primer on Linear Models
Book
03/2008
1st Edition
Chapman & Hall/CRC
€94.10
Shipment within 15-20 days
Person
Content
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.
