Statistical analysis with missing data
Statistical analysis with missing data
Analysis of correlated binary data under partially linear single-index logistic models
Journal of Multivariate Analysis
Empirical likelihood for semiparametric regression model with missing response data
Journal of Multivariate Analysis
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In this article we study a semiparametric generalized partially linear model when the covariates are missing at random. We propose combining local linear regression with the local quasilikelihood technique and weighted estimating equation to estimate the parameters and nonparameters when the missing probability is known or unknown. We establish normality of the estimators of the parameter and asymptotic expansion for the estimators of the nonparametric part. We apply the proposed models and methods to a study of the relation between virologic and immunologic responses in AIDS clinical trials, in which virologic response is classified into binary variables. We also give simulation results to illustrate our approach.