Spatial modelling for mixed-state observations

Cécile Hardouin; Jian-Feng Yao

hal-00205584, version 1

Spatial modelling for mixed-state observations

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

Electronic Journal of Statistics 2 (2008) 213-233

Abstract: In several application fields like daily pluviometry data modelling, or motion analysis from image sequences, observations contain two components of different nature. A first part is made with discrete values accounting for some symbolic information and a second part records a continuous (real-valued) measurement. We call such type of observations "mixed-state observations". This paper introduces spatial models suited for the analysis of these kinds of data. We consider multi-parameter auto-models whose local conditional distributions belong to a mixed state exponential family. Specific examples with exponential distributions are detailed, and we present some experimental results for modelling motion measurements from video sequences.

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-00205584, version 1
http://hal.archives-ouvertes.fr/hal-00205584
oai:hal.archives-ouvertes.fr:hal-00205584
From: Jian-Feng Yao <>
Submitted on: Wednesday, 16 January 2008 13:22:03
Updated on: Tuesday, 27 April 2010 17:31:22

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arXiv: 0801.2231

DOI: 10.1214/08-EJS173

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