Curve registration by nonparametric goodness-of-fit testing

Arnak S. Dalalyan (1, 2) , Olivier Collier (1, 2)

1 imagine [Marne-la-Vallée]
2 LIGM - Laboratoire d'Informatique Gaspard-Monge

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 squared test. We also prove that the proposed test is consistent, i.e., its power is asymptotically equal to 1. Some numerical experiments on synthetic datasets are reported as well.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Arnak S. Dalalyan, Olivier Collier. Curve registration by nonparametric goodness-of-fit testing. 2011. ⟨hal-00580047v1⟩