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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

WIKS: a general Bayesian nonparametric index for quantifying differences between two populations

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Author(s):
Ceregatti, Rafael de Carvalho [1] ; Izbicki, Rafael [1] ; Bueno Salasar, Luis Ernesto [1]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Carlos, Rod Washington Luis Km 235, SP-310, Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: TEST; MAY 2020.
Web of Science Citations: 0
Abstract

A key problem in many research investigations is to decide whether two samples have the same distribution. Numerous statistical methods have been devoted to this issue, but only few considered a Bayesian nonparametric approach. In this paper, we propose a novel nonparametric Bayesian index (WIKS) for quantifying the difference between two populations P1 and P2, which is defined by a weighted posterior expectation of the Kolmogorov-Smirnov distance between P1 and P2. We present a Bayesian decision-theoretic argument to support the use ofWIKS index and a simple algorithm to compute it. Furthermore, we prove that WIKS is a statistically consistent procedure and that it controls the significance level uniformly over the null hypothesis, a feature that simplifies the choice of cutoff values for taking decisions. We present a real data analysis and an extensive simulation study showing that WIKS is more powerful than competing approaches under several settings. (AU)

FAPESP's process: 19/11321-9 - Neural networks in statistical inference problems
Grantee:Rafael Izbicki
Support type: Regular Research Grants
FAPESP's process: 17/03363-8 - Interpretability and efficiency in hypothesis tests
Grantee:Rafael Izbicki
Support type: Regular Research Grants