Mixture versus loglinear models in contingency table analysis

RAPALLO Fabio

Mixture versus loglinear models in contingency table analysis

RAPALLO Fabio

Research output: Contribution to conference › Paper › peer-review

Abstract

We study here two different approaches to model a cluster structure in contingency tables. From a classical independence model, we consider an additional pattern structure given by a family of subsets of cells where we allow a specific mean parameter. We encode such structure through mixture models and loglinear models, and we compare the algebraic equations defining these classes of models. We discuss some examples, and we characterize the models in some special cases.

Original language	English
Pages	1-6
Number of pages	6
Publication status	Published - 1 Jan 2013
Externally published	Yes
Event	Advances in Latent Variables - Methods, Models and Applications - Brescia Duration: 1 Jan 2013 → …

Conference

Conference	Advances in Latent Variables - Methods, Models and Applications
City	Brescia
Period	1/01/13 → …

Cite this

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title = "Mixture versus loglinear models in contingency table analysis",

abstract = "We study here two different approaches to model a cluster structure in contingency tables. From a classical independence model, we consider an additional pattern structure given by a family of subsets of cells where we allow a specific mean parameter. We encode such structure through mixture models and loglinear models, and we compare the algebraic equations defining these classes of models. We discuss some examples, and we characterize the models in some special cases.",

author = "RAPALLO Fabio",

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N2 - We study here two different approaches to model a cluster structure in contingency tables. From a classical independence model, we consider an additional pattern structure given by a family of subsets of cells where we allow a specific mean parameter. We encode such structure through mixture models and loglinear models, and we compare the algebraic equations defining these classes of models. We discuss some examples, and we characterize the models in some special cases.

AB - We study here two different approaches to model a cluster structure in contingency tables. From a classical independence model, we consider an additional pattern structure given by a family of subsets of cells where we allow a specific mean parameter. We encode such structure through mixture models and loglinear models, and we compare the algebraic equations defining these classes of models. We discuss some examples, and we characterize the models in some special cases.

UR - https://iris.uniupo.it/handle/11579/33964

M3 - Paper

SP - 1

EP - 6

T2 - Advances in Latent Variables - Methods, Models and Applications

Y2 - 1 January 2013

ER -

Mixture versus loglinear models in contingency table analysis

Abstract

Conference

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