On nonparametric conditional independence tests for continuous variables
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AbstractTesting conditional independence (CI) for continuous variables is a fundamental but challenging task in statistics. Many tests for this task are developed and used increasingly widely by data analysts. This article reviews the current status of the nonparametric part of these tests, which assumes no parametric form for the joint continuous density function. The different ways to approach the CI are summarized. Tests are also grouped according to their data assumptions and method types. A numerical comparison is also conducted for representative tests.
This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
Acceptance Date22/10/2019
All Author(s) ListLi C., Fan X.
Journal nameWiley Interdisciplinary Reviews: Computational Statistics
Year2020
Month5
Volume Number12
Issue Number3
PublisherWiley: 12 months
Article numbere1489
ISSN1939-5108
eISSN1939-0068
LanguagesEnglish-United Kingdom
Keywordsconditional independence, hypothesis testing, literature review

Last updated on 2020-19-10 at 00:48