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Empirical Likelihood for Partially Non Linear Models with Missing Response Variables at Random

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  • Yanting Xiao
  • Zheng Tian
  • Wenyan Guo

Abstract

This article is concerned with partially non linear models when the response variables are missing at random. We examine the empirical likelihood (EL) ratio statistics for unknown parameter in non linear function based on complete-case data, semiparametric regression imputation, and bias-corrected imputation. All the proposed statistics are proven to be asymptotically chi-square distribution under some suitable conditions. Simulation experiments are conducted to compare the finite sample behaviors of the proposed approaches in terms of confidence intervals. It showed that the EL method has advantage compared to the conventional method, and moreover, the imputation technique performs better than the complete-case data.

Suggested Citation

  • Yanting Xiao & Zheng Tian & Wenyan Guo, 2015. "Empirical Likelihood for Partially Non Linear Models with Missing Response Variables at Random," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(16), pages 3523-3540, August.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:16:p:3523-3540
    DOI: 10.1080/03610926.2013.815211
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