Statistical Inference in Context Specific Interaction Models for Contingency Tables

Author: Højsgaard S.

Source: Scandinavian Journal of Statistics, Volume 31, Number 1, March 2004 , pp. 143-158(16)

Buy & download fulltext article:

Price: $48.00 plus tax (Refund Policy)

Abstract:

Context specific interaction models is a class of interaction models for contingency tables in which interaction terms are allowed to vanish in specific contexts given by the levels of sets of variables. Such restrictions can entail conditional independencies which only hold for some values of the conditioning variables and allows also for irrelevance of some variables in specific contexts. A Markov property is established and so is an iterative proportional scaling algorithm for maximum likelihood estimation. Decomposition of the estimation problem is treated and model selection is discussed.

Keywords: conditional independence; context specific independence; contingency table; decomposition; graphical model; hierarchical model; iterative proportional scaling; log-linear model; Markov property

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9469.2004.00378.x

Affiliations: 1: Danish Institute of Agricultural Sciences

Publication date: 2004-03-01