Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Generalized estimating equations with stabilized working correlation structure

Authors
Kwon, YongchanChoi, Young-GeunPark, TaesungZiegler, AndreasPaik, Myunghee Cho
Issue Date
Feb-2017
Publisher
ELSEVIER SCIENCE BV
Citation
COMPUTATIONAL STATISTICS DATA ANALYSIS, v.106, pp 1 - 11
Pages
11
Journal Title
COMPUTATIONAL STATISTICS DATA ANALYSIS
Volume
106
Start Page
1
End Page
11
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146233
DOI
10.1016/j.csda.2016.08.016
ISSN
0167-9473
1872-7352
Abstract
Generalized estimating equations (GEE) proposed by Liang and Zeger (1986) yield a consistent estimator for the regression parameter without correctly specifying the correlation structure of the repeatedly measured outcomes. It is well known that the efficiency of regression coefficient estimator increases with correctly specified working correlation and thus unstructured correlation could be a good candidate. However, lack of positive-definiteness of the estimated correlation matrix in unbalanced case causes practitioners to choose independent, autoregressive or exchangeable matrices as working correlation structure. Our goal is to broaden practical choices of working correlation structure to unstructured correlation matrix or any other matrices by proposing a GEE with a stabilized working correlation matrix via linear shrinkage method in which the minimum eigenvalue is forced to be bounded below by a small positive number. We show that the resulting regression estimator of GEE is asymptotically equivalent to that of the original GEE. Simulation studies show that the proposed modification can stabilize the variance of the GEE regression estimator with unstructured working correlation, and improve efficiency over popular choices of working correlation. Two real data examples are presented where the standard error
Files in This Item
Go to Link
Appears in
Collections
이과대학 > 통계학과 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE