Pseudo likelihood and dimension reduction for data with nonignorable nonresponse

Ji Chen, Bingying Xie and Jun Shao

Statistical Theory and Related Fields, 2018, vol. 2, issue 2, 196-205

Abstract: Tang et al. (2003. Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 90(4), 747–764) and Zhao & Shao (2015. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data. Journal of the American Statistical Association, 110(512), 1577–1590) proposed a pseudo likelihood approach to estimate unknown parameters in a parametric density of a response Y conditioned on a vector of covariate X, where Y is subjected to nonignorable nonersponse, X is always observed, and the propensity of whether or not Y is observed conditioned on Y and X is completely unspecified. To identify parameters, Zhao & Shao (2015. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data. Journal of the American Statistical Association, 110(512), 1577–1590) assumed that X can be decomposed into U and Z, where Z can be excluded from the propensity but is related with Y even conditioned on U. The pseudo likelihood involves the estimation of the joint density of U and Z. When this density is estimated nonparametrically, in this paper we apply sufficient dimension reduction to reduce the dimension of U for efficient estimation. Consistency and asymptotic normality of the proposed estimators are established. Simulation results are presented to study the finite sample performance of the proposed estimators.

Date: 2018
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DOI: 10.1080/24754269.2018.1516101

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