## Model Checking for Incomplete High Dimensional Categorical Data (1999)

### BibTeX

@MISC{Hu99modelchecking,

author = {Ming-yi Hu},

title = {Model Checking for Incomplete High Dimensional Categorical Data},

year = {1999}

}

### OpenURL

### Abstract

Categorical data are often arranged in a contingency table and summarized by a loglinear model. A standard approach for comparing two competing models is to calculate twice the discrepancy between maximized loglikelihoods, which follows a 2 distribution asymptotically. But when data are sparse, the 2 approximation may be questionable. As an alternative to a large-sample approximation to the reference distribution, we implement the framework introduced by Rubin (1984) for finding the posterior predictive check (PPC) distribution. The PPC distribution represents the conditional probability of a future value of a test statistic based on the information given by observed data along with model specifications, which can se...