PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On applications of marginal models for categorical data
Tamas Rudas and Wicher Bergsma
Metron Volume 17, Number 1, pp. 1-25, 2004.

Abstract

The paper considers marginal models for categorical data and after reviewing the most important theoretical results concerning the definition, estimation and testing of such models, discusses a number of common statistical problems. These examples include, among others, the analysis of repeated measurements, panel studies and missing data. Fitting marginal models in these cases has the potential of providing the researcher with substantial new insight. The examples illustrate that the marginal modeling approach may be used more widely than thought before. One of the examples shows howgraphical models associated with directed acyclic graphs can be parameterized. A general algorithm is presented to compute maximum likelihood estimates under marginal models

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:822
Deposited By:Wicher Bergsma
Deposited On:01 January 2005