HYPOTHESIS-TESTING OF REGRESSION PARAMETERS IN SEMIPARAMETRIC GENERALIZED LINEAR-MODELS FOR CLUSTER CORRELATED DATA
成果类型：
Article
署名作者：
ROTNITZKY, A; JEWELL, NP
署名单位：
University of California System; University of California Berkeley; University of California System; University of California Berkeley
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.2307/2336986
发表日期：
1990
页码：
485497
关键词：
摘要：
Generalized and ''working''Wald and score tests for regression coefficients in the class of semiparametric marginal generalized linear models for cluster correlated data (Liang and Zeger, 1986) are proposed, and their asymptotic distribution examined. In addition, the asymptotic distribution of the naive likelihood ratio test, or deviance difference, is presented. Following Rao and Scott (1984), we propose simple adjustments to such ''working'' tests. The asymptotic distributions of the ''working'' tests allow us to explore theoretical bounds on the ratios of the robust variance of the regression parameter estimators and their naive variance counterparts computed assuming independent observations. In addition, the adequacy of a particular choice of working correlation structure is considered. We illustrate our results with a numerical example.