2012 AISTATS AISTATS 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 (Wilks’ phenomenon). We also prove that the proposed test is consistent, extiti.e., its power is asymptotically equal to 1. Finite sample properties of the proposed methodology are demonstrated by numerical simulations.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — likelihood ratio test
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Interdisciplinary, Machine Learning, Mathematics & Optimization