Nonparametric tests for conditional independence in two-way contingency tables

Gery Geenens and Leopold Simar

Journal of Multivariate Analysis, 2010, vol. 101, issue 4, 765-788

Abstract: Testing for the independence between two categorical variables R and S forming a contingency table is a well-known problem: the classical chi-square and likelihood ratio tests are used. Suppose now that for each individual a set of p characteristics is also observed. Those explanatory variables, likely to be associated with R and S, can play a major role in their possible association, and it can therefore be interesting to test the independence between R and S conditionally on them. In this paper, we propose two nonparametric tests which generalise the chi-square and the likelihood ratio ideas to this case. The procedure is based on a kernel estimator of the conditional probabilities. The asymptotic law of the proposed test statistics under the conditional independence hypothesis is derived; the finite sample behaviour of the procedure is analysed through some Monte Carlo experiments and the approach is illustrated with a real data example.

Keywords: Two-way; contingency; tables; Chi-square; test; Likelihood; ratio; test; Nonparametric; regression; Conditional; independence (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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