Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data
Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data
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 …