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Mixed Graphical Models with Missing Data and the Partial Imputation EM Algorithm

Zhi Geng, Kang Wan and Feng Tao

Scandinavian Journal of Statistics, 2000, vol. 27, issue 3, 433-444

Abstract: In this paper we discuss graphical models for mixed types of continuous and discrete variables with incomplete data. We use a set of hyperedges to represent an observed data pattern. A hyperedge is a set of variables observed for a group of individuals. In a mixed graph with two types of vertices and two types of edges, dots and circles represent discrete and continuous variables respectively. A normal graph represents a graphical model and a hypergraph represents an observed data pattern. In terms of the mixed graph, we discuss decomposition of mixed graphical models with incomplete data, and we present a partial imputation method which can be used in the EM algorithm and the Gibbs sampler to speed their convergence. For a given mixed graphical model and an observed data pattern, we try to decompose a large graph into several small ones so that the original likelihood can be factored into a product of likelihoods with distinct parameters for small graphs. For the case that a graph cannot be decomposed due to its observed data pattern, we can impute missing data partially so that the graph can be decomposed.

Date: 2000
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Citations: View citations in EconPapers (6)

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https://doi.org/10.1111/1467-9469.00199

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