Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

10-2018

Journal or Book Title

Statistica Sinica

Volume

28

Issue

4

First Page

1907

Last Page

1928

DOI

10.5705/ss.202016.0317

Abstract

We propose a method for linear mixed effects models when the covariates are completely observed but the outcome of interest is subject to missing under cluster-specific nonignorable (CSNI) missingness. Our strategy is to replace missing quantities in the full-data objective function with unbiased predictors derived from inverse probability weighting and calibration technique. The proposed approach can be applied to estimating equations or likelihood functions with modified E-step, and does not require numerical integration as do previous methods. Unlike usual inverse probability weighting, the proposed method does not require correct specification of the response model as long as the CSNI assumption is correct, and renders inference under CSNI without a full distributional assumption. Consistency and asymptotic normality are shown with a consistent variance estimator. Simulation results and a data example are presented.

Comments

This article is published as Y. Kwon, J.K. Kim, M.C. Paik, and H. Kim (2018). A robust calibration-assisted method for linear mixed effects model under cluster-specific nonignorable missingness. Statistica Sinica, 28, 1907-1928. doi: 10.5705/ss.202016.0317. Posted with permission.

Copyright Owner

Institute of Statistical Science, Academia Sinica

Language

en

File Format

application/pdf

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