Electronic Journal of Statistics

Spatial modelling for mixed-state observations

Cécile Hardouin and Jian-Feng Yao

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 2 (2008), 213-233.

Dates
First available in Project Euclid: 27 March 2008

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1206641967

Digital Object Identifier
doi:10.1214/08-EJS173

Mathematical Reviews number (MathSciNet)
MR2386093

Zentralblatt MATH identifier
1135.62043

Subjects
Primary: 62H05: Characterization and structure theory 62E10: Characterization and structure theory
Secondary: 62M40: Random fields; image analysis

Keywords
Multivariate analysis Distribution theory Mixed-state variables Auto-models Spatial cooperation Markov random fields

Citation

Hardouin, Cécile; Yao, Jian-Feng. Spatial modelling for mixed-state observations. Electron. J. Statist. 2 (2008), 213--233. doi:10.1214/08-EJS173. https://projecteuclid.org/euclid.ejs/1206641967


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