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Conditional Independence Models which are Totally Ordered
Indian Statistical Institute, Bangalore, India.
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering. Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
2018 (English)Report (Other academic)
Abstract [en]

The totally ordered conditional independence (TOCI) model N(K) is defined to be the set of all normal distributions on RI such that for each adjacent pair (Ki, Ki+1)  K, the components of a multivariate normal vector x  RI, indexed by the set difference { Ki+1 \ Ki } are mutually conditionally independent given the variables indexed by Ki. Here K = {K1  …  Kq } is a totally ordered set of subsets of a finite index set I. It is shown that TOCI models constitute a proper subset of lattice conditional independence (LCI) models. It follows that like LCI models, for the TOCI models the likelihood function and parameter space can be factored into the products of conditional likelihood functions and disjoint parameter spaces, respectively, where each conditional likelihood function corresponds to an ordinary multivariate normal regression model. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. , p. 15
Series
LiTH-MAT-R, ISSN 0348-2960 ; 2018:9
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-150009ISRN: LiTH-MAT-R--2018/09--SEOAI: oai:DiVA.org:liu-150009DiVA, id: diva2:1237244
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-10-08Bibliographically approved

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CiteExportLink to record
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  • apa
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