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Statistical Inference on Partially Linear Additive Models with Missing Response Variables and Error-prone Covariates

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  • Chuan-Hua Wei
  • Xu-Jie Jia
  • Hong-Sheng Hu

Abstract

This paper considers statistical inference for the partially linear additive models, which are useful extensions of additive models and partially linear models. We focus on the case where some covariates are measured with additive errors, and the response variable is sometimes missing. We propose a profile least-squares estimator for the parametric component and show that the resulting estimator is asymptotically normal. To construct a confidence region for the parametric component, we also propose an empirical-likelihood-based statistic, which is shown to have a chi-squared distribution asymptotically. Furthermore, a simulation study is conducted to illustrate the performance of the proposed methods.

Suggested Citation

  • Chuan-Hua Wei & Xu-Jie Jia & Hong-Sheng Hu, 2015. "Statistical Inference on Partially Linear Additive Models with Missing Response Variables and Error-prone Covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(4), pages 872-883, February.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:4:p:872-883
    DOI: 10.1080/03610926.2012.735327
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