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โœฆ   LIBER   โœฆ

๐Ÿ“

Linear Algebra and Linear Models

โœ Scribed by R. B. Bapat


Publisher
Springer
Year
2000
Tongue
English
Leaves
146
Series
Universitext
Edition
2nd
Category
Library

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โœฆ Synopsis


Bapat has created an extremely useful reference on/guide to estimation methods and linear models. The first chapter is a very concise linear algebra review which is really nice (who can remember all that stuff?). The scope is wide enough to get singular decomposition, principal components, and rank additivity. Very useful for beginning statisticians and researchers like myself. My only complaint is that the proofs are often cursory and rather terse, leaving out many "obvious" steps, and sometimes complex assertions are made with no proof whatsoever. However, many other texts provide these proofs such as Arnold's "Theory of Linear Models and Multivariate Analysis."


๐Ÿ“œ SIMILAR VOLUMES


Linear Algebra and Linear Models
โœ R.B. Bapat ๐Ÿ“‚ Library ๐Ÿ“… 2000 ๐Ÿ› Springer ๐ŸŒ English

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Linear Algebra and Linear Models
โœ R. B. Bapat ๐Ÿ“‚ Library ๐Ÿ“… 2000 ๐Ÿ› Springer ๐ŸŒ English

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โœ R.B. Bapat (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2012 ๐Ÿ› Springer-Verlag London ๐ŸŒ English

<p><p>Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear algebra required for statistics followed by the basic aspects of the theory of linear estimation and hypothesis testing. The emphasis is on the approach using generalized inverses. Topics such as the multi

Linear Algebra and Linear Models
โœ R.B. Bapat (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2012 ๐Ÿ› Springer-Verlag London ๐ŸŒ English

<p><p>Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear algebra required for statistics followed by the basic aspects of the theory of linear estimation and hypothesis testing. The emphasis is on the approach using generalized inverses. Topics such as the multi

Linear Algebra and Linear Models
โœ R.B. Bapat (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2012 ๐Ÿ› Springer-Verlag London ๐ŸŒ English

<p><p>Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear algebra required for statistics followed by the basic aspects of the theory of linear estimation and hypothesis testing. The emphasis is on the approach using generalized inverses. Topics such as the multi