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Activity Number: 653
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #316888
Title: A Finite Mixture Logistic-Gaussian Model for Zero-Inflated Clustered Binary Data
Author(s): John Kwagyan* and Victor Apprey and Nana Osafo
Companies: Howard University and Howard University and Howard University
Keywords: Clustered binary data ; Logistic-Gaussian model ; random effect models ; zero-inflated models ; Gaussian quadratures
Abstract:

We establish a finite mixture model for clustered binary data in which all members of clusters in one latent class have a zero response with probability one; and clusters in the other latent class yield correlated outcomes. Response probabilities in terms of fixed effect and random effects models are formulated, and estimation procedures based on Gaussian Quadrature and a combined EM with Gaussian Quadrature are developed. Application to esophageal cancer data in Chinese families is presented.


Authors who are presenting talks have a * after their name.

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