A class of smooth models satisfying marginal and context specific conditional independencies

R. Colombi and A. Forcina

Journal of Multivariate Analysis, 2014, vol. 126, issue C, 75-85

Abstract: We study a class of conditional independence models for discrete data with the property that one or more log-linear interactions are defined within two different marginal distributions and then constrained to 0; all the conditional independence models which are known to be non-smooth belong to this class. We introduce a new marginal log-linear parameterization and show that smoothness may be restored by restricting one or more independence statements to hold conditionally to a restricted subset of the configurations of the conditioning variables. Our results are based on a specific reconstruction algorithm from log-linear parameters to probabilities and fixed point theory. Several examples are examined and a general rule for determining the implied conditional independence restrictions is outlined.

Keywords: Categorical data; Marginal log-linear parameterizations; Smooth parameterizations (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.jmva.2014.01.001

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