Multi-parameter auto-models and their application

Cécile Hardouin; Jian-Feng Yao

hal-00154382, version 1

Multi-parameter auto-models and their application

Cécile Hardouin () ^1, 2, Jian-Feng Yao () ³

Biometrika 95, 2 (2008) 335-349

Abstract: Motivated by the modelling of non Gaussian data or positively correlated data on a lattice, extensions of Besag's Markov random fields auto-models to exponential families with multi-dimensional parameters have been proposed recently. In this paper, we provide a multiple-parameter analog of Besag's one-dimensional result that gives the necessary form of the exponential families for the Markov random field's conditional distributions. We propose estimation of parameters by maximum pseudo-likelihood and give a proof for the consistency of the estimators for the multi-parameter auto-model. The methodology is illustrated with some examples, particularly the building of a cooperative system with beta conditional distributions.

1: Statistique Appliquée et MOdélisation Stochastique (SAMOS)
Université Paris I - Panthéon-Sorbonne
2: Centre d'économie de la Sorbonne (CES)
CNRS : UMR8174 – Université Paris I - Panthéon-Sorbonne
3: Institut de Recherche Mathématique de Rennes (IRMAR)
CNRS : UMR6625 – Université de Rennes 1 – École normale supérieure de Cachan - ENS Cachan – Institut National des Sciences Appliquées (INSA) : - RENNES – Université de Rennes II - Haute Bretagne

hal-00154382, version 1
http://hal.archives-ouvertes.fr/hal-00154382
oai:hal.archives-ouvertes.fr:hal-00154382
From: Jian-Feng Yao <>
Submitted on: Wednesday, 13 June 2007 14:25:29
Updated on: Tuesday, 27 April 2010 17:35:08

See detailed view

Associated documents

PDF :

DOI: 10.1093/biomet/asn016

Export

Bibtex EndNote TEI RefWorks

<?xml version="1.0" encoding="UTF-8"?>
<listBibl xmlns="http://www.tei-c.org/ns/1.0" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML">
<listBibl type="article"><head>Articles in peer-reviewed journal</head>
<biblStruct type="article" xml:id="hal-00154382"><analytic>
<author><persName><forename>Cécile</forename><surname>Hardouin</surname></persName>
<affiliation>
<orgName type="laboratory">SAMOS</orgName>
<orgName type="team">Statistique Appliquée et MOdélisation Stochastique</orgName>
<country>FR</country>
<orgName type="institution">Université Paris I - Panthéon-Sorbonne</orgName>
</affiliation>
<affiliation>
<orgName type="laboratory">CES</orgName>
<orgName type="team">Centre d'économie de la Sorbonne</orgName>
<country>FR</country>
<orgName type="institution">CNRS : UMR8174</orgName>
<orgName type="institution">Université Paris I - Panthéon-Sorbonne</orgName>
</affiliation>
</author>
<author><persName><forename>Jian-Feng</forename><surname>Yao</surname></persName>
<affiliation>
<orgName type="laboratory">IRMAR</orgName>
<orgName type="team">Institut de Recherche Mathématique de Rennes</orgName>
<country>FR</country>
<orgName type="institution">CNRS : UMR6625</orgName>
<orgName type="institution">Université de Rennes 1</orgName>
<orgName type="institution">École normale supérieure de Cachan - ENS Cachan</orgName>
<orgName type="institution">Institut National des Sciences Appliquées (INSA) : - RENNES</orgName>
<orgName type="institution">Université de Rennes II - Haute Bretagne</orgName>
</affiliation>
</author>
<title type="main" level="a">Multi-parameter auto-models and their application</title></analytic><monogr><title level="j" type="main">Biometrika</title><imprint><biblScope type="publicationDate">2008-06-00</biblScope><biblScope type="vol">95</biblScope><biblScope type="issue">2</biblScope><biblScope type="pp">335-349</biblScope></imprint></monogr><idno type="doi">10.1093/biomet/asn016</idno><idno type="HALid">http://hal.archives-ouvertes.fr/hal-00154382</idno><idno type="HALFile">http://hal.archives-ouvertes.fr/hal-00154382/PDF/HY-revised.pdf</idno></biblStruct>
</listBibl>
</listBibl>