Conditional independence models for seemingly unrelated regressions with incomplete data

Authors:
Mathias Drton;Steen A. Andersson;Michael D. Perlman
Affiliations:
Department of Statistics, The University of Chicago, Chicago, IL, USA;Department of Mathematics, Indiana University, Bloomington, IN, USA;Department of Statistics, University of Washington, Seattle, WA, USA
Venue:
Journal of Multivariate Analysis
Year:
2006

Citing 4
Cited 1

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Testing lattice conditional independence models

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A graphical characterization of lattice conditional independence models

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Computing all roots of the likelihood equations of seemingly unrelated regressions

Journal of Symbolic Computation

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Abstract

We consider normal = Gaussian seemingly unrelated regressions (SUR) with incomplete data (ID). Imposing a natural minimal set of conditional independence constraints, we find a restricted SUR/ID model whose likelihood function and parameter space factor into the product of the likelihood functions and the parameter spaces of standard complete data multivariate analysis of variance models. Hence, the restricted model has a unimodal likelihood and permits explicit likelihood inference. In the development of our methodology, we review and extend existing results for complete data SUR models and the multivariate ID problem.