- Series: Springer Series in Statistics
- Hardcover: 427 pages
- Publisher: Springer; 2nd edition (July 30, 1999)
- Language: English
- ISBN-10: 0387988483
- ISBN-13: 978-0387988481
- Product Dimensions: 6.1 x 1.1 x 9.2 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 3 customer reviews
- Amazon Best Sellers Rank: #2,905,058 in Books (See Top 100 in Books)
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Linear Models: Least Squares and Alternatives (Springer Series in Statistics) 2nd Edition
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"This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses instatistics at the graduate level as well as an accompanying text for other courses in which linear models play a part."
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I bought the book based on dataguru's amazon recommendation and a subsequent email correspondence. I was not disappointed. The book starts out covering the classical linear models and regression but then goes on to cover problems involving fixed and stochastic constraints. Also although Chapter 3 starts out with least squares regression it goes on to cover projection pursuit, censored regression and includes various alternative estimation procedures other than least squares. In the case of colinearity, principal components regression,ridge regression and shrinkage estimators are offered. Nonparametric regression, logistic regression and neural networks are all covered in this amazing Chapter 3.
The text provides a very current and thorough list of relevant references. Other nice features of this second edition include a completely revised and updated chapter on missing data, much of the unusual material in Chapter 3 including the restricted regression and neural networks, Kalman filtering in Chapter 6 and the use of empirical Bayes methods for simultaneous solution of parameter estimates in different linear models in Chapter 4.
This book will be a treasured reference source. I may have to search through it carefully to discover hidden treasures. Rao does that with his conciseness. I found that "Linear Statistical Inference and Its Applications" had a lot more to offer than I first thought. It was a required text for my mathematical statistics course at Stanford but served more as a reference than as a course text. When taking the course I did not find time to use it much. But many years later I looked through it and was amazed at all the deep and important theoretical results that were included in it. I expect the same from this book.