Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property
摘要
The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.
引用
@article{arxiv.math/0508266,
title = {Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property},
author = {Mathias Drton and Michael Eichler},
journal= {arXiv preprint arXiv:math/0508266},
year = {2010}
}
备注
15 pages, article will appear in Scandinavian Journal of Statistics