A note on deletion diagnostics for estimating equations

John S. Preisser; Bahjat F. Qaqish; Jamie Perin

doi:10.1093/biomet/asn019

A note on deletion diagnostics for estimating equations

John S. Preisser, Bahjat F. Qaqish, Jamie Perin

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

7 Scopus citations

Abstract

We describe an algorithm based upon the Sherman-Morrison-Woodbury formula for the inversion of matrices with special structure that occur in formulae for deletion diagnostics. Substantial computational savings relative to a method based upon Cholesky's decomposition are illustrated. The result has broad application to regression diagnostics for clustered data.

Original language	English (US)
Pages (from-to)	509-513
Number of pages	5
Journal	Biometrika
Volume	95
Issue number	2
DOIs	https://doi.org/10.1093/biomet/asn019
State	Published - Jun 2008
Externally published	Yes

Keywords

Generalized estimating equation
Influence
Matrix inversion
One-step approximation
Regression diagnostic

ASJC Scopus subject areas

Statistics and Probability
Mathematics(all)
Agricultural and Biological Sciences (miscellaneous)
Agricultural and Biological Sciences(all)
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1093/biomet/asn019

Cite this

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title = "A note on deletion diagnostics for estimating equations",

abstract = "We describe an algorithm based upon the Sherman-Morrison-Woodbury formula for the inversion of matrices with special structure that occur in formulae for deletion diagnostics. Substantial computational savings relative to a method based upon Cholesky's decomposition are illustrated. The result has broad application to regression diagnostics for clustered data.",

keywords = "Generalized estimating equation, Influence, Matrix inversion, One-step approximation, Regression diagnostic",

author = "Preisser, {John S.} and Qaqish, {Bahjat F.} and Jamie Perin",

note = "Funding Information: This research was supported by a grant from the U.S. National Institutes of Health.",

year = "2008",

month = jun,

doi = "10.1093/biomet/asn019",

language = "English (US)",

volume = "95",

pages = "509--513",

journal = "Biometrika",

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T1 - A note on deletion diagnostics for estimating equations

AU - Preisser, John S.

AU - Qaqish, Bahjat F.

AU - Perin, Jamie

N1 - Funding Information: This research was supported by a grant from the U.S. National Institutes of Health.

PY - 2008/6

Y1 - 2008/6

N2 - We describe an algorithm based upon the Sherman-Morrison-Woodbury formula for the inversion of matrices with special structure that occur in formulae for deletion diagnostics. Substantial computational savings relative to a method based upon Cholesky's decomposition are illustrated. The result has broad application to regression diagnostics for clustered data.

AB - We describe an algorithm based upon the Sherman-Morrison-Woodbury formula for the inversion of matrices with special structure that occur in formulae for deletion diagnostics. Substantial computational savings relative to a method based upon Cholesky's decomposition are illustrated. The result has broad application to regression diagnostics for clustered data.

KW - Generalized estimating equation

KW - Influence

KW - Matrix inversion

KW - One-step approximation

KW - Regression diagnostic

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U2 - 10.1093/biomet/asn019

DO - 10.1093/biomet/asn019

M3 - Article

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SP - 509

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JO - Biometrika

JF - Biometrika

IS - 2

ER -

A note on deletion diagnostics for estimating equations

Abstract

Keywords

ASJC Scopus subject areas

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