title: Partially Linear Models with Missing Response Variables and Error-prone Covariates.
abstract: We consider partially linear models of the form Y = X(T)beta + nu(Z) + epsilon when the response variable Y is sometimes missing with missingness probability pi depending on (X, Z), and the covariate X is measured with error, where nu(z) is an unspecified smooth function. The missingness structure is therefore missing not at random, rather than the usual missing at random. We propose a class of semiparametric estimators for the parameter of interest beta, as well as for the population mean E(Y). The resulting estimators are shown to be consistent and asymptotically normal under general assumptions. To construct a confidence region for beta, we also propose an empirical-likelihood-based statistic, which is shown to have a chi-squared distribution asymptotically. The proposed methods are applied to an AIDS clinical trial dataset. A simulation study is also reported.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York 14642, U.S.A.
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.forename: Suojin
authors.initials: S
authors.lastname: Wang
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll