A Model Searching Method based on Marginal Model Structures

S.-H. Kim and S. Lee (Korea)


Combined model structure, Graph-separateness, Interac tion graph; Markovian subgraph, Prime separator.


Suppose that we are interested in modeling for a random vector X and that we are given a set of graphical decomposable models, G1, · · · , Gm, for subvectors of X each of which share some variables with at least one of the other models. Under the assumption that the model of X is graphical and decomposable, we propose an approach of searching for models of X based on the given decompos able graphical models. A main idea in this approach is that we combine G1, · · · , Gm using graphs of prime separators (section 2). When the true graphical model for the whole data is decomposable, prime separators in a marginal model are also prime separators in a maximal combined model of the marginal models. This property plays a key role in model-combination. The proposed approach is applied to searching for a model of 100 variables for illustration.

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