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Independence test of a continuous random variable and a discrete random variable

Title
Independence test of a continuous random variable and a discrete random variable
Authors
Yang J.Kim M.
Ewha Authors
김미정
SCOPUS Author ID
김미정scopus
Issue Date
2020
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 27, no. 3, pp. 285 - 299
Keywords
CausationIndependence testKernel density estimationKolmogorov-Smirnov test
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
Abstract
In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test. © 2020 The Korean Statistical Society, and Korean International Statistical Society.
DOI
10.29220/CSAM.2020.27.3.285
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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