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Journal Articles Journal of Machine Learning Research Year : 2012

Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration

Abstract

The problem of curve registration appears in many different areas of applications ranging from neuroscience to road traffic modeling. In the present work, we propose a nonparametric testing framework in which we develop a generalized likelihood ratio test to perform curve registration. We first prove that, under the null hypothesis, the resulting test statistic is asymptotically distributed as a chi-squared random variable. This result, often referred to as Wilks' phenomenon, provides a natural threshold for the test of a prescribed asymptotic significance level and a natural measure of lack-of-fit in terms of the p-value of the $\chi^2$-test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Finite sample properties of the proposed methodology are demonstrated by numerical simulations.
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Dates and versions

hal-00705796 , version 1 (08-06-2012)

Identifiers

  • HAL Id : hal-00705796 , version 1

Cite

Olivier Collier, Arnak S. Dalalyan. Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration. Journal of Machine Learning Research, 2012, 22, pp.264-272. ⟨hal-00705796⟩
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