## 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.

Original language | English |
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Article number | 285 |

Pages (from-to) | 285-299 |

Number of pages | 15 |

Journal | Communications for Statistical Applications and Methods |

Volume | 27 |

Issue number | 3 |

DOIs | |

State | Published - 2020 |

### Bibliographical note

Publisher Copyright:© 2020 The Korean Statistical Society, and Korean International Statistical Society.

## Keywords

- Causation
- Independence test
- Kernel density estimation
- Kolmogorov-Smirnov test