Open Access
2008 Spatial modelling for mixed-state observations
Cécile Hardouin, Jian-Feng Yao
Electron. J. Statist. 2: 213-233 (2008). DOI: 10.1214/08-EJS173


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.


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Cécile Hardouin. Jian-Feng Yao. "Spatial modelling for mixed-state observations." Electron. J. Statist. 2 213 - 233, 2008.


Published: 2008
First available in Project Euclid: 27 March 2008

zbMATH: 1135.62043
MathSciNet: MR2386093
Digital Object Identifier: 10.1214/08-EJS173

Primary: 62E10 , 62H05
Secondary: 62M40

Keywords: Auto-models , distribution theory , Markov random fields , Mixed-state variables , Multivariate analysis , Spatial cooperation

Rights: Copyright © 2008 The Institute of Mathematical Statistics and the Bernoulli Society

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