HYPOTHESIS-TESTING OF REGRESSION PARAMETERS IN SEMIPARAMETRIC GENERALIZED LINEAR-MODELS FOR CLUSTER CORRELATED DATA
HYPOTHESIS-TESTING OF REGRESSION PARAMETERS IN SEMIPARAMETRIC GENERALIZED LINEAR-MODELS FOR CLUSTER CORRELATED DATA
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DOI：
10.1093/biomet/77.3.485
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发表时间：
1990-09-01
期刊：
影响因子：
2.7
通讯作者：
JEWELL, NP
中科院分区：
文献类型：
--
作者：
ROTNITZKY, A;JEWELL, NP
本文提出了一类半参数边际广义线性模型回归系数的广义和“工作”Wald检验和Score检验(梁和Zeger，1986)，并研究了它们的渐近分布.此外，还给出了朴素似然比检验或偏差差的渐近分布.继Rao和Scott(1984)之后，我们对这种“工作”检验提出了简单的调整.‘’工作‘’检验的渐近分布使我们能够探索回归参数估计量的稳健方差比与其在独立观测下计算的朴素方差对应项之比的理论界。此外，还考虑了工作相关结构的特定选择的充分性。我们用一个数值例子来说明我们的结果。
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.