Parameterization and estimation of path models for categorical data

Tamás Rudas (), Wicher Bergsma () and Renáta Németh ()
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Tamás Rudas: Eötvös Loránd University, Department of Statistics, Faculty of Social Sciences
Wicher Bergsma: London School of Economics and Political Science, Department of Statistics
Renáta Németh: Eötvös Loránd University, Department of Statistics, Faculty of Social Sciences

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 383-394 from Springer

Abstract: Abstract The paper discusses statistical models for categorical data based on directed acyclic graphs (DAGs) assuming that only effects associated with the arrows of the graph exist. Graphical models based on DAGs are similar, but allow the existence of effects not directly associated with any of the arrows. Graphical models based on DAGs are marginal models and are best parameterized by using hierarchical marginal log-linear parameters. Path models are defined here by assuming that all hierarchical marginal log-linear parameters not associated with an arrow are zero, providing a parameterization with straightforward interpretation. The paper gives a brief review of log-linear, graphical and marginal models, presents a method for the maximum likelihood estimation of path models and illustrates the use of path models, with special emphasis on the interpretation of estimated parameter values, to real data.

Keywords: Categorical data; DAG; marginal model; parameterization; path model (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_30

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DOI: 10.1007/978-3-7908-1709-6_30

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