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SHORT COMMUNICATIONS

Pseudo likelihood and dimension reduction for data with nonignorable nonresponse

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Pages 196-205 | Received 23 Apr 2018, Accepted 22 Aug 2018, Published online: 01 Sep 2018
 

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Division of Mathematical Sciences [1612873] and the Chinese Ministry of Education 111 Project [B14019].

Notes on contributors

Ji Chen

Ji Chen is a PhD candidate in East China Normal University.

Bingying Xie

Bingying Xie is a statistician at Roche in Shanghai, China.

Jun Shao

Jun Shao is a professor in University of Wisconsin-Madison.

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