LATTICE MODELS FOR CONDITIONAL-INDEPENDENCE IN A MULTIVARIATE NORMAL-DISTRIBUTION
成果类型：
Article
署名作者：
ANDERSSON, SA; PERLMAN, MD
署名单位：
University of Washington; University of Washington Seattle
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1176349261
发表日期：
1993
页码：
1318-1358
关键词：
摘要：
The lattice conditional independence model N(K) is defined to be the set of all normal distributions on R(I) such that for every pair L, M is-an-element-of K, x(L) and x(M) are conditionally independent given x(L and M). Here K is a ring of subsets of the finite index set I and, for K is-an-element-of K, x(K) is the coordinate projection of x is-an-element-of R(I) onto R(K). Statistical properties of N(K) may be studied, for example, maximum likelihood inference, invariance and the problem of testing H-0: N(K) vs. H: N(M) when M is a subring of K. The set J(K) of join-in-educible elements of K plays a central role in the analysis of N(K). This class of statistical models occurs in the analysis of nonnested multivariate missing data patterns and nonnested dependent linear regression models.
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