Title :
A new nonparametric measure of conditional independence
Author :
Seth, Sohan ; Park, Il ; Principe, José C.
Author_Institution :
Comput. NeuroEngineering Lab., Univ. of Florida, Gainesville, FL
Abstract :
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.
Keywords :
approximation theory; estimation theory; conditional independence; conditional marginal density functions; distance measurement; estimator approximation; kernel density estimation; nonparametric measurement; smoothing function; Bandwidth; Density functional theory; Density measurement; Gain measurement; Kernel; Neural engineering; Parameter estimation; Random variables; Robustness; Smoothing methods; Causality; Gaussian integral; conditional independence; dimension reduction; multivariate density estimation;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4960250