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Title: Minimax nonparametric multi-sample test under smoothing Authors:  Xin Xing - Virginia Tech University (United States)
Zuofeng Shang - New Jersey Institute of Technology (United States)
Pang Du - Virginia Tech (United States) [presenting]
Ping Ma - University of Georgia (United States)
WenXuan Zhong - University of Georgia (United States)
Jun Liu - Harvard University (United States)
Abstract: The problem of comparing probability densities among multiple groups is considered. A new probabilistic tensor product smoothing spline framework is developed to model the joint density of two variables. Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions. We propose a penalized likelihood ratio test for such interaction testing and show that the test statistic is asymptotically chi-square distributed under the null hypothesis. Furthermore, we derive a sharp minimax testing rate based on the Bernstein width for nonparametric multi-sample tests and show that our proposed test statistic is minimax optimal. In addition, a data-adaptive tuning criterion is developed to choose the penalty parameter. Simulations and real applications demonstrate that the proposed test outperforms the conventional approaches under various scenarios.