The Cholesky factorization of the inverse correlation or covariance matrix in multiple regression

Douglas M. Hawkins; W. J.R. Eplett

doi:10.1080/00401706.1982.10487758

The Cholesky factorization of the inverse correlation or covariance matrix in multiple regression

Douglas M. Hawkins, W. J.R. Eplett

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

The lower-triangular Cholesky inverse root (CIR) of the correlation matrix of the dependent and independent variables in a multiple regression problem is shown to be an excellent summary statistic yielding information about the full multiple regression and subsets. In particular, the CIR may be used to select variables and identify alternative subsets. It is also useful for interrelationship analysis.

Original language	English (US)
Pages (from-to)	191-198
Number of pages	8
Journal	Technometrics
Volume	24
Issue number	3
DOIs	https://doi.org/10.1080/00401706.1982.10487758
State	Published - Aug 1982

Keywords

Interactive computing
Latent-root regression analysis
Regression
Subset selection

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10.1080/00401706.1982.10487758

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abstract = "The lower-triangular Cholesky inverse root (CIR) of the correlation matrix of the dependent and independent variables in a multiple regression problem is shown to be an excellent summary statistic yielding information about the full multiple regression and subsets. In particular, the CIR may be used to select variables and identify alternative subsets. It is also useful for interrelationship analysis.",

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year = "1982",

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AU - Eplett, W. J.R.

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AB - The lower-triangular Cholesky inverse root (CIR) of the correlation matrix of the dependent and independent variables in a multiple regression problem is shown to be an excellent summary statistic yielding information about the full multiple regression and subsets. In particular, the CIR may be used to select variables and identify alternative subsets. It is also useful for interrelationship analysis.

KW - Interactive computing

KW - Latent-root regression analysis

KW - Regression

KW - Subset selection

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The Cholesky factorization of the inverse correlation or covariance matrix in multiple regression

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