Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data

  • Lizhong Ding Inception Institute of Artificial Intelligence
  • Zhi Liu University of Macau
  • Yu Li King Abdullah University of Science and Technology
  • Shizhong Liao Tianjin University
  • Yong Liu Chinese Academy of Sciences
  • Peng Yang King Abdullah University of Science and Technology
  • Ge Yu Chinese Academy of Sciences
  • Ling Shao Inception Institute of Artificial Intelligence
  • Xin Gao King Abdullah University of Science and Technology

Abstract

We propose a framework for analyzing and comparing distributions without imposing any parametric assumptions via empirical likelihood methods. Our framework is used to study two fundamental statistical test problems: the two-sample test and the goodness-of-fit test. For the two-sample test, we need to determine whether two groups of samples are from different distributions; for the goodness-of-fit test, we examine how likely it is that a set of samples is generated from a known target distribution. Specifically, we propose empirical likelihood ratio (ELR) statistics for the two-sample test and the goodness-of-fit test, both of which are of linear time complexity and show higher power (i.e., the probability of correctly rejecting the null hypothesis) than the existing linear statistics for high-dimensional data. We prove the nonparametric Wilks’ theorems for the ELR statistics, which illustrate that the limiting distributions of the proposed ELR statistics are chi-square distributions. With these limiting distributions, we can avoid bootstraps or simulations to determine the threshold for rejecting the null hypothesis, which makes the ELR statistics more efficient than the recently proposed linear statistic, finite set Stein discrepancy (FSSD). We also prove the consistency of the ELR statistics, which guarantees that the test power goes to 1 as the number of samples goes to infinity. In addition, we experimentally demonstrate and theoretically analyze that FSSD has poor performance or even fails to test for high-dimensional data. Finally, we conduct a series of experiments to evaluate the performance of our ELR statistics as compared to state-of-the-art linear statistics.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning