The Annals of Statistics

Invariant normal models with recursive graphical Markov structure

Jesper Madsen

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Abstract

An extension of the class of GS-LCI normal models introduced by Andersson and Madsen is defined and studied. The models are defined in terms of symmetry restrictions given by a finite group and conditional independence restrictions given by an acyclic directed graph. Maximum likelihood estimation of the parameters in the models is discussed.

Article information

Source
Ann. Statist., Volume 28, Number 4 (2000), 1150-1178.

Dates
First available in Project Euclid: 12 March 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1015956711

Digital Object Identifier
doi:10.1214/aos/1015956711

Mathematical Reviews number (MathSciNet)
MR1810923

Zentralblatt MATH identifier
1105.62346

Subjects
Primary: 62H12: Estimation 62H15: Hypothesis testing
Secondary: 62H10: Distribution of statistics 62H20: Measures of association (correlation, canonical correlation, etc.) 62A05

Keywords
Group symmetry invariance orthogonal group representation acyclic directed graph conditional independence graphical Markov structure maximum likelihood estimator multivariate normal distribution

Citation

Madsen, Jesper. Invariant normal models with recursive graphical Markov structure. Ann. Statist. 28 (2000), no. 4, 1150--1178. doi:10.1214/aos/1015956711. https://projecteuclid.org/euclid.aos/1015956711


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References

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