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Activity Number: 260 - Statistical Methods for Handling Imperfect Data Subject to Missing and/or Mismeasurement
Type: Invited
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #314457
Title: Full-Semiparametric-Likelihood-Based Inference for Nonignorable Missing Data
Author(s): Pengfei Li*
Companies: University of Waterloo
Keywords: Density ratio model; Empirical likelihood; Identifiability; Maximum likelihood estimation; Non-ignorable missing data

During the past few decades, missing-data problems have been studied extensively, with a focus on the ignorable missing case, where the missing probability depends only on observable quantities. By contrast, research into non-ignorable missing data problems is quite limited. The main difficulty in solving such problems is that the missing probability and the regression likelihood function are tangled together in the likelihood presentation, and the model parameters may not be identifiable even under strong parametric model assumptions. In this paper we discuss a semiparametric model for data with non-ignorable missing responses and propose a maximum full semiparametric likelihood estimation method, which is an efficient combination of the parametric conditional likelihood and the marginal nonparametric biased sampling likelihood. We further show that the proposed estimators for the underlying parameters and the response mean are semiparametrically efficient. Extensive simulations and a real data analysis demonstrate the advantage of the proposed method over competing methods.

Authors who are presenting talks have a * after their name.

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