Comparisons of tests of distributional assumption in Poisson regression model


ÖZONUR D. , AKDUR H. T. K. , BAYRAK H.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.46, no.8, pp.6197-6207, 2017 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 8
  • Publication Date: 2017
  • Doi Number: 10.1080/03610918.2016.1202267
  • Title of Journal : COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Page Numbers: pp.6197-6207
  • Keywords: Goodness of fit, Poisson regression model, Power rates, Type I error, GOODNESS-OF-FIT, OVERDISPERSION

Abstract

Count data consists of discrete non-negative integer values. Poisson regression model is one of the most popular model used to model count data. This model assumes that response variable has Poisson distribution. The purpose of this article is to assess distributional assumption of this model by using some goodness of fit tests. These tests are compared in respect to type I error and power rates of tests with different samples, parameters and sample sizes. Simulation study suggests that the most powerful tests are generally Dean-Lawless and Cameron-Trivedi score tests.