Curve registration by Nonparametric goodness-of-fit Testing

Olivier Collier () and Arnak Dalalyan
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Olivier Collier: Université Marne la Vallée

No 2013-33, Working Papers from Center for Research in Economics and Statistics

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 x2-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. As an application, a new local descriptor for digital images is introduced and an experimental evaluation of its discriminative power is conducted

Keywords: nonparametric inference; hypotheses testing; Wilks’ phenomenon; keypoint matching (search for similar items in EconPapers)
Pages: 26
Date: 2013-12
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Citations: View citations in EconPapers (3)

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