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
SN - 2287-7843
VL - 27
SP - 285
EP - 299
JO - Communications for Statistical Applications and Methods
JF - Communications for Statistical Applications and Methods
IS - 3
M1 - 285
ER -