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Nuisance parameter elimination for proportional likelihood ratio models with nonignorable missingness and random truncation

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  • Kwun Chuen Gary Chan

Abstract

We show that the proportional likelihood ratio model proposed recently by Luo & Tsai (2012) enjoys model-invariant properties under certain forms of nonignorable missing mechanisms and randomly double-truncated data, so that target parameters in the population can be estimated consistently from those biased samples. We also construct an alternative estimator for the target parameters by maximizing a pseudolikelihood that eliminates a functional nuisance parameter in the model. The corresponding estimating equation has a U-statistic structure. As an added advantage of the proposed method, a simple score-type test is developed to test a null hypothesis on the regression coefficients. Simulations show that the proposed estimator has a small-sample efficiency similar to that of the nonparametric likelihood estimator and performs well for certain nonignorable missing data problems. Copyright 2013, Oxford University Press.

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  • Kwun Chuen Gary Chan, 2013. "Nuisance parameter elimination for proportional likelihood ratio models with nonignorable missingness and random truncation," Biometrika, Biometrika Trust, vol. 100(1), pages 269-276.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:1:p:269-276
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    File URL: http://hdl.handle.net/10.1093/biomet/ass056
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    Cited by:

    1. Bhaskar Bhattacharya & Mohammad Al-talib, 2017. "A minimum relative entropy based correlation model between the response and covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1095-1118, September.
    2. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.

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