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Linear Models for Unbalanced Data

Shayle R. Searle
John Wiley
Publication Date: 
Number of Pages: 
Wiley Series in Probability and Statistics
[Reviewed by
Ita Cirovic Donev
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Linear Models for Unbalanced Data is a classic that has now been reprinted in soft covers. Books that make it to the classics shelf tend to be quite terse even though excellent in what they set out to explain. This book is a classic, but it is not terse at all; quite the contrary. The author provides more than enough motivation for the subject and does not push the reader away by simply stating definitions. There are plenty of examples scattered throughout the text. The examples are presented in great detail, blending very nicely with the theoretical presentation. Calculations are not left to the reader but are actually presented. The level of mathematics is intermediate, i.e., there are no strict theorem-proof discussions, but the author explains each topic separately with a little subchapter.

Given the number of detailed examples I would recommend this book to undergraduate students as well as graduates. The book is very easily to follow; anyone with a good background in undergraduate statistics and linear models should have no trouble learning from it. With appropriate background and great interest, this book can even be used for self-study.

Ita Cirovic Donev is a PhD candidate at the University of Zagreb. She hold a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical mehods of credit and market risk. Apart from the academic work she does consulting work for financial institutions.

1. An Up-Dated Viewpoint: Cell Means Models.

2. Basic Results for Cell Means Models: The 1-Way Classification.

3. Nested Classifications.

4. The 2-Way Crossed Classification with All-Cells-Filled Data: Cell Means Models.

5. The 2-Way Classification with Some-Cell Empty Data: Cell Means Models.

6. Models with Covariables (Analysis of Covariance): the 1-Way Classification.

7. Matrix Algebra and Quadratic Forms ( A Prelude to Chapter 8).

8. A General Linear Model.

9. The 2-Way Crossed Classification: Overparameterized Models.

10. Extended Cell Means Models.

11. Models with Covariables: The General Case and Some Applications.

12. Comments on Computing Packages.

13. Mixed Models: A Thumbnail Survey.


Statistical Tables.

List of Tables and Figures.