Kernel-based independence test aims to compare the embedding difference of distributions between the joint distribution and the product of marginals in the RKHS ...
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We propose a novel framework for kernel-based statistical independence tests that enable adaptatively learning parameterized kernels to maximize test power.
A new class of kernels is designed that can adaptatively focus on the significant dimensions of variables to judge independence, which makes the tests more ...
Abstract. In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an ...
Conditional independence (CI) test stands as a fundamental and challenging task within modern statistics and machine learning. One pivotal class of methods.
Sep 10, 2024 · We propose a scheme for selecting the kernels used in an HSIC-based independence test, based on maximizing an estimate of the asymptotic test power.
Missing: Adaptive Statistical
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference ...
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A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference ...
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference ...
Oct 15, 2016 · Abstract:A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed.