Deletion diagnostics for marginal mean and correlation model parameters in estimating equations

John S. Preisser; Jamie Perin

doi:10.1007/s11222-007-9031-1

Deletion diagnostics for marginal mean and correlation model parameters in estimating equations

John S. Preisser, Jamie Perin

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

7 Scopus citations

Abstract

Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations. Estimating equations for intracluster correlations facilitate computational formulae for one-step deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model. The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model. The diagnostics are applied to data from four research studies from public health and medicine.

Original language	English (US)
Pages (from-to)	381-393
Number of pages	13
Journal	Statistics and Computing
Volume	17
Issue number	4
DOIs	https://doi.org/10.1007/s11222-007-9031-1
State	Published - Dec 2007
Externally published	Yes

Keywords

Cluster trial
Generalized estimating equations
Influence
Intracluster correlation
Logit models

ASJC Scopus subject areas

Theoretical Computer Science
Statistics and Probability
Statistics, Probability and Uncertainty
Computational Theory and Mathematics

Access to Document

10.1007/s11222-007-9031-1

Cite this

@article{098a371b835242c3855b0509505c8389,

title = "Deletion diagnostics for marginal mean and correlation model parameters in estimating equations",

abstract = "Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations. Estimating equations for intracluster correlations facilitate computational formulae for one-step deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model. The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model. The diagnostics are applied to data from four research studies from public health and medicine.",

keywords = "Cluster trial, Generalized estimating equations, Influence, Intracluster correlation, Logit models",

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

note = "Funding Information: Acknowledgements This research was supported by grants 98-AH-F8-0101, 98-AH-F8-0101 (S-1) and 98-AH-F8-0101 (S-2) of the Office of Juvenile Justice and Delinquency Prevention, U.S. Department of Justice, and grant CA101901 from the U.S. National Institutes of Health. Copyright: Copyright 2007 Elsevier B.V., All rights reserved.",

year = "2007",

month = dec,

doi = "10.1007/s11222-007-9031-1",

language = "English (US)",

volume = "17",

pages = "381--393",

journal = "Statistics and Computing",

issn = "0960-3174",

publisher = "Springer Netherlands",

number = "4",

}

TY - JOUR

T1 - Deletion diagnostics for marginal mean and correlation model parameters in estimating equations

AU - Preisser, John S.

AU - Perin, Jamie

N1 - Funding Information: Acknowledgements This research was supported by grants 98-AH-F8-0101, 98-AH-F8-0101 (S-1) and 98-AH-F8-0101 (S-2) of the Office of Juvenile Justice and Delinquency Prevention, U.S. Department of Justice, and grant CA101901 from the U.S. National Institutes of Health. Copyright: Copyright 2007 Elsevier B.V., All rights reserved.

PY - 2007/12

Y1 - 2007/12

N2 - Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations. Estimating equations for intracluster correlations facilitate computational formulae for one-step deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model. The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model. The diagnostics are applied to data from four research studies from public health and medicine.

AB - Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations. Estimating equations for intracluster correlations facilitate computational formulae for one-step deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model. The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model. The diagnostics are applied to data from four research studies from public health and medicine.

KW - Cluster trial

KW - Generalized estimating equations

KW - Influence

KW - Intracluster correlation

KW - Logit models

UR - http://www.scopus.com/inward/record.url?scp=34548580309&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548580309&partnerID=8YFLogxK

U2 - 10.1007/s11222-007-9031-1

DO - 10.1007/s11222-007-9031-1

M3 - Article

AN - SCOPUS:34548580309

SN - 0960-3174

VL - 17

SP - 381

EP - 393

JO - Statistics and Computing

JF - Statistics and Computing

IS - 4

ER -

Deletion diagnostics for marginal mean and correlation model parameters in estimating equations

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this