Spatial modelling for mixed-state observations

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

1 SAMOS - Statistique Appliquée et MOdélisation Stochastique
2 CES - Centre d'économie de la Sorbonne
3 IRMAR - Institut de Recherche Mathématique de Rennes

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.

Mots clés: Markov random fields.; Multivariate analysis; Distribution theory; Mixed-state variables; Auto-models; Spatial cooperation; Markov random fields

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Cécile Hardouin, Jian-Feng Yao. Spatial modelling for mixed-state observations. Electronic Journal of Statistics , 2008, 2, pp.213-233. ⟨10.1214/08-EJS173⟩. ⟨hal-00205584⟩