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Semiparametric inference with kernel likelihood

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  • Ao Yuan

Abstract

We study a class of semiparametric likelihood models in which parameters are incorporated explicitly, with the unknown likelihood specified nonparametrically by the kernel estimator. The maximum likelihood estimator (MLE) under this semiparametric model is used for inference of the parameters. The method is a generalisation of the semiparametric regression model we proposed recently. Such semiparametric models are robust, and MLEs under these likelihoods are shown to be consistent, asymptotic normal with rate √n and possess Wilks property.

Suggested Citation

  • Ao Yuan, 2009. "Semiparametric inference with kernel likelihood," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(2), pages 207-228.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:2:p:207-228
    DOI: 10.1080/10485250802553382
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    References listed on IDEAS

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    Cited by:

    1. Chen, Yixin & Wang, Qin & Yao, Weixin, 2015. "Adaptive estimation for varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 17-31.
    2. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    3. Zhang, Jun & Lin, Bingqing & Zhou, Yan, 2021. "Kernel density estimation for partial linear multivariate responses models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    4. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    5. Wang, Qin & Yao, Weixin, 2012. "An adaptive estimation of MAVE," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 88-100, February.
    6. Alan Huang, 2013. "Density estimation and nonparametric inferences using maximum likelihood weighted kernels," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 561-571, September.

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