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February 2001 Generalized Likelihood Ratio Statistics and Wilks Phenomenon
Jianqing Fan, Chunming Zhang, Jian Zhang
Ann. Statist. 29(1): 153-193 (February 2001). DOI: 10.1214/aos/996986505

Abstract

Likelihood ratio theory has had tremendous success in parametric inference, due to the fundamental theory of Wilks. Yet, there is no general applicable approach for nonparametric inferences based on function estimation. Maximum likelihood ratio test statistics in general may not exist in nonparametric function estimation setting. Even if they exist, they are hard to find and can not be optimal as shown in this paper. We introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. A new Wilks phenomenon is unveiled. We demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are asymptotically distribution free and follow χ2-distributions under null hypotheses for a number of useful hypotheses and a variety of useful models including Gaussian white noise models, nonparametric regression models, varying coefficient models and generalized varying coefficient models. We further demonstrate that generalized likelihood ratio statistics are asymptotically optimal in the sense that they achieve optimal rates of convergence given by Ingster. They can even be adaptively optimal in the sense of Spokoiny by using a simple choice of adaptive smoothing parameter. Our work indicates that the generalized likelihood ratio statistics are indeed general and powerful for nonparametric testing problems based on function estimation.

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Jianqing Fan. Chunming Zhang. Jian Zhang. "Generalized Likelihood Ratio Statistics and Wilks Phenomenon." Ann. Statist. 29 (1) 153 - 193, February 2001. https://doi.org/10.1214/aos/996986505

Information

Published: February 2001
First available in Project Euclid: 5 August 2001

zbMATH: 1029.62042
MathSciNet: MR1833962
Digital Object Identifier: 10.1214/aos/996986505

Subjects:
Primary: 62G07
Secondary: 62G10, 62J12

Rights: Copyright © 2001 Institute of Mathematical Statistics

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Vol.29 • No. 1 • February 2001
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