Feb 17, 2020 · In this study, we propose non-parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein ...
In this study, we propose non- parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein discrepancy. Our method ...
Feb 17, 2020 · In this study, we propose non-parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein ...
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Feb 17, 2020 · This study proposes non-parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein ...
A Stein Goodness-of-fit Test for Directional Distributions. A Uniformity test ... Kuiper test for uniformity is based on the cumulative distribution function (cdf) ...
In this study, we propose non-parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein discrepancy. Our method ...
A Stein Goodness-of-fit Test for Directional Distributions. W Xu, T Matsuda. AISTATS2020, 2020. 22, 2020. Kernelized Stein discrepancy tests of goodness-of-fit ...
Abstract. We propose a goodness-of-fit measure for proba- bility densities modeling observations with vary- ing dimensionality, such as text documents of ...
Mar 11, 2021 · A famous generic method for approximating distributions and quantifying discrepancy and manufacturing concentration bounds and limit theorems is Stein's method.
PDF | In this paper, we develop a simple non-parametric test for testing normal distribution based on the distance between empirical zero-bias.
Missing: Directional | Show results with:Directional