Online Program Home
My Program

Abstract Details

Activity Number: 693
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #318896 View Presentation
Title: Analysis of Bivariate Zero-Inflated Count Data with Missing Responses
Author(s): Miao Yang* and Kalyan Das and Anandamayee Majumdar
Companies: Oregon State University and Calcutta University and Soochow University
Keywords: bivariate zero-inflated Poisson ; generalized linear model ; MCEM algorithm ; missing data
Abstract:

Bivariate zero-inflated Poisson regression models have recently been used in various medical and biological settings to model excess zeros. However, there has not been any definite approach to deal with the same in the event of missing responses. The model itself is complex and as the responses are paired, missing values can occur in either or both coordinates. We propose a flexible Monte Carlo expectation maximization based approach to handle bivariate zero inflated count data with missing responses. We report the results of a simulation study designed to evaluate the performance of the proposed approach. To illustrate the application of our model and methodology, we consider a bivariate data concerning the demand for health care in Australia.


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association