JSM 2004 - Toronto

Abstract #300679

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Activity Number: 330
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #300679
Title: Semiparametric Regression Models for Zero-inflated Data
Author(s): Sujit K. Ghosh*+
Companies: North Carolina State University
Address: , Raleigh, NC, 27695-8203,
Keywords: overdispersion ; regression models ; semiparametric ; zero-inflated data
Abstract:

In recent years there has been considerable interest in models for count data that allow for overdispersion due to excess zeros. These models (e.g., ZIP) complement more conventional models for overdispersion that are based on the mean-variance relationship. However, most of the regression models for zero-inflated data involve full parametric specification of the distribution of counts and hence these models may fail to address various features of count data. The proposed semiparametric model is flexible and accounts for overdispersion due to excess zeros. Inference is based on estimating equations derived under a semiparametric framework. Simulation studies are provided to validate the use of proposed model and a real data application is presented for illustration.

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