Fitting Linear Models: An Application of Conjugate Gradient Algorithms

✍ Scribed by Allen Mclntosh (auth.)

Publisher: Springer-Verlag New York
Year: 1982
Tongue: English
Leaves: 207
Series: Lecture Notes in Statistics 10
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

The increasing power and decreasing price of smalI computers, especialIy "personal" computers, has made them increasingly popular in statistical analysis. The day may not be too far off when every statistician has on his or her desktop computing power on a par with the large mainframe computers of 15 or 20 years ago. These same factors make it relatively easy to acquire and manipulate large quantities of data, and statisticians can expect a corresponding increase in the size of the datasets that they must analyze. Unfortunately, because of constraints imposed by architecture, size or price, these smalI computers do not possess the main memory of their large cousins. Thus, there is a growing need for algorithms that are sufficiently economical of space to permit statistical analysis on smalI computers. One area of analysis where there is a need for algorithms that are economical of space is in the fitting of linear models.

✦ Table of Contents

Front Matter....Pages i-vi
Preliminaries....Pages 1-7
The Linear Model....Pages 8-25
The Conjugate Gradient Algorithm....Pages 26-40
Applications: The Non-Full Rank Case....Pages 41-67
Applications: The Full Rank Case....Pages 68-101
Examples: Gaussian Linear Models....Pages 102-115
Examples: Generalized Linear Models....Pages 116-124
Concluding Remarks....Pages 125-126
Back Matter....Pages 127-201

✦ Subjects

Statistics, general

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