JSM 2004 - Toronto

Abstract #300445

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Activity Number: 233
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 12:00 PM to 1:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #300445
Title: Inflated Generalized Poisson Regression Models
Author(s): Felix Famoye*+ and Karan P. Singh
Companies: Central Michigan University and University of North Texas
Address: Dept. of Mathematics, Mount Pleasant, MI, 48859-0001,
Keywords: count data ; k-Inflation ; estimation ; hypothesis-testing
Abstract:

Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for modeling data with too many zeros. A zero-inflated generalized Poisson regression model is proposed as a good alternate to model count data with too many zeros. We propose a k-inflated generalized Poisson regression (k-IGPR) to model count data with too many k-values. Estimation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of k-values is too large for the generalized Poisson model to adequately fit the data. The k-IGPR model is illustrated using a numerical dataset. We used the ordinary generalized Poisson regression (GPR), the k-inflated Poisson regression (k-IPR), and the k-IGPR to model the response variable. We found that the k-IGPR model is more appropriate than the k-IPR model for the data.

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