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Empirical likelihood inferences for varying coefficient partially nonlinear models

Author

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  • Xiaoshuang Zhou
  • Peixin Zhao
  • Xiuli Wang

Abstract

In this article, empirical likelihood inferences for the varying coefficient partially nonlinear models are investigated. An empirical log-likelihood ratio function for the unknown parameter vector in the nonlinear function part and a residual-adjusted empirical log-likelihood ratio function for the nonparametric component are proposed. The corresponding Wilks phenomena are proved and the confidence regions for parametric component and nonparametric component are constructed. Simulation studies indicate that, in terms of coverage probabilities and average areas of the confidence regions, the empirical likelihood method performs better than the normal approximation-based method. Furthermore, a real data set application is also provided to illustrate the proposed empirical likelihood estimation technique.

Suggested Citation

  • Xiaoshuang Zhou & Peixin Zhao & Xiuli Wang, 2017. "Empirical likelihood inferences for varying coefficient partially nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(3), pages 474-492, February.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:474-492
    DOI: 10.1080/02664763.2016.1177496
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    Cited by:

    1. Xiaoshuang Zhou & Peixin Zhao, 2022. "Estimation and inferences for varying coefficient partially nonlinear quantile models with censoring indicators missing at random," Computational Statistics, Springer, vol. 37(4), pages 1727-1750, September.

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