NDLI: Compatibility of conditionally specified models

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Compatibility of conditionally specified models

Content Provider	PubMed Central
Author	Chen, Hua Yun
Abstract	A conditionally specified joint model is convenient to use in fields such as spatial data modeling, Gibbs sampling, and missing data imputation. One potential problem with such an approach is that the conditionally specified models may be incompatible, which can lead to serious problems in applications. We propose an odds ratio representation of a joint density to study the issue and derive conditions under which conditionally specified distributions are compatible and yield a joint distribution. Our conditions are the simplest to verify compared with those proposed in the literature. The proposal also explicitly construct joint densities that are fully compatible with the conditionally specified densities when the conditional densities are compatible, and partially compatible with the conditional densities when they are incompatible. The construction result is then applied to checking the compatibility of the conditionally specified models. Ways to modify the conditionally specified models based on the construction of the joint models are also discussed when the conditionally specified models are incompatible.
Related Links	http://dx.doi.org/10.1016/j.spl.2009.12.025
Ending Page	677
Page Count	8
Starting Page	670
File Format	PDF
ISSN	01677152
Journal	Statistics & probability letters
Issue Number	7-8
Volume Number	80
Language	English
Publisher Date	2010-04-01
Access Restriction	Open
Subject Keyword	Statistics, Probability and Uncertainty Statistics and Probability Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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