Imputation based statistical inference for partially linear quantile regression models with missing responses

Peixin Zhao () and Xinrong Tang
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Peixin Zhao: Chongqing Technology and Business University
Xinrong Tang: Chongqing Technology and Business University

Metrika: International Journal for Theoretical and Applied Statistics, 2016, vol. 79, issue 8, No 5, 1009 pages

Abstract: Abstract In this paper, we consider the confidence interval construction for partially linear quantile regression models with missing response at random. We propose an imputation based empirical likelihood method to construct confidence intervals for the parametric components and the nonparametric components, and show that the proposed empirical log-likelihood ratios are both asymptotically Chi-squared in theory. Then, the confidence region for the parametric component and the pointwise confidence interval for the nonparametric component are constructed. Some simulation studies and a real data application are carried out to assess the performance of the proposed estimation method, and simulation results indicate that the proposed method is workable.

Keywords: Quantile regression; Partially linear model; Empirical likelihood; Missing data; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00184-016-0586-8

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