TY - JOUR

T1 - Independence test of a continuous random variable and a discrete random variable

AU - Yang, Jinyoung

AU - Kim, Mijeong

N1 - Funding Information:
Mijeong Kim was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (NRF-2017R1C1B5015186).
Publisher Copyright:
© 2020 The Korean Statistical Society, and Korean International Statistical Society.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Causation

KW - Independence test

KW - Kernel density estimation

KW - Kolmogorov-Smirnov test

UR - http://www.scopus.com/inward/record.url?scp=85087677948&partnerID=8YFLogxK

U2 - 10.29220/CSAM.2020.27.3.285

DO - 10.29220/CSAM.2020.27.3.285

M3 - Article

AN - SCOPUS:85087677948

VL - 27

SP - 285

EP - 299

JO - Communications for Statistical Applications and Methods

JF - Communications for Statistical Applications and Methods

SN - 2287-7843

IS - 3

M1 - 285

ER -