Abstract #300291

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JSM 2003 Abstract #300291
Activity Number: 214
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #300291
Title: Inference for Nonrecursive Graphical Model Mixed with Discrete and Censored Continuous Clustered Responses
Author(s): Dabao Zhang*+ and Martin T. Wells
Companies: Cornell University and Cornell University
Address: 301 Mallott Hall, Ithaca, NY, 14853-4201,
Keywords: graphical model ; mixed graphical models ; reciprocal graph ; Expectation-Maximization algorithm ; stochastic version ; nonrecursive model
Abstract:

Nonrecursive graphical models mixed with discrete and continuous responses are investigated by using latent conditional Gaussian simultaneous equation models for clustered data. For latent simultaneous equation models in both cross-sectional study and panel study, the full-information maximum likelihood estimation is considered by constructing stochastic-version EM algorithms: Monte Carlo EM, stochastic EM, and stochastic approximation EM. The well-known Developmental Toxicity Study data is reinvestigated by using the latent simultaneous equation models and the designed algorithm; results suggest some new notions about the exogeneity of one of the response variables of interest. The proposed methods also apply to censored (or truncated) continuous responses as well as to discrete responses.

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