Here - Tilburg University
Here - Tilburg University
Here - Tilburg University
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Author and presenter<br />
Ark van der, Andries; <strong>Tilburg</strong> School of Social and Behavioral Sciences<br />
Title<br />
Categorical Marginal Models for Large Sparse Contingency Tables<br />
Abstract<br />
Categorical marginal models (CMMs) are flexible tools to model location,<br />
spread, and association in categorical data that have some dependence<br />
structure.The categorical data are collected in a contingency table; location,<br />
spread, or association are modelled by restricting certain marginals of the<br />
contingency table. If contingency tables are large, maximum likelihood<br />
estimation of the CMMs is no longer feasible due to computer memory problems.<br />
We propose a maximum empirical likelihood estimation (MEL) procedure for<br />
estimating CMMs for large contingency tables, and discuss three related<br />
problems:The problem of finding the correct design matrices and the so-called<br />
empty set problem can be solved satisfactorily, the problem of obtaining good<br />
starting values remains unsolved. A simulation study shows that for small data<br />
contingency tables ML and MEL yield comparable estimates. For large tables,<br />
when ML does not work, MEL has a good sensitivity and specificity if good<br />
starting values are available.
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