A new nonparametric measure of conditional independence

Sohan Seth, Il Park, Jose C Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we propose a new measure of conditional independence that is loosely based on measuring the L2 distance between the conditional joint and the product of the conditional marginal density functions. However, we propose to smooth the arguments prior to measuring the distance and use kernel density estimation to derive the estimator. We show that under suitable conditions the proposed smoothing does not affect the conditional independence but using proper smoothing function helps in choosing the bandwidth parameter robustly. We discuss the computational issues and propose an approximation to evaluate the estimator efficiently. We apply the proposed measure in different experiments to show its validity.
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)978-1-4244-2354-5
ISBN (Print)978-1-4244-2353-8
Publication statusPublished - 19 Apr 2009


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