Spatial modelling for mixed-state observations

被引：1

作者：

Hardouin, Cecile ^{[1
]}

Yao, Jian-Feng ^{[2
]}

机构：

[1] Univ Paris 01, SAMOS MATISSE Ctr Econ Sorbonne, F-75634 Paris 13, France

[2] Univ Rennes 1, IRMAR, F-35042 Rennes, France

来源：

ELECTRONIC JOURNAL OF STATISTICS | 2008年 / 2卷

关键词：

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

D O I：

10.1214/08-EJS173

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：213 / 233

页数：21

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