Testing conditional independence via Rosenblatt transforms

Abstract

This paper proposes new tests of conditional independence of two random variables given a single-index involving an unknown finite-dimensional parameter. The tests employ Rosenblatt transforms and are shown to be distribution-free while retaining computational convenience. Some results from Monte Carlo simulations are presented and discussed.

Published in at http://dx.doi.org/10.1214/09-AOS704 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• Nonparametric checks for single-index models
• Quotient correlation: A sample based alternative to Pearson's correlation

Cited by in corpus (2)

• Testing Functional Inequalities
• Dependence structure estimation using Copula Recursive Trees