Comparing Poisson-Inverse Gaussian Model and Negative Binomial Model on case study: Horseshoe crabs data

Research output: Contribution to journalConference article

Abstract

Poisson Regression analysis is commonly used for dependent variable that has non-negative value, called count data. Poisson Regression has an assumption that mean of dependent variable equal to its variance. On over dispersion case where the variance is greater than mean, poisson regression is inconvenient to used because it may underestimate the standard error of regression parameters and consequently giving misleading inference. Poisson-Inverse Gaussian and Negative Binomial regression model can be used on over dispersion data. This paper will discuss about Poisson-Inverse Gaussian regression model, Negative Binomial regression model and comparing them in terms of Goodness-of-fit (GOF) statistics on case study of horseshoe crabs data. According to the result, pseudo R-squared value of P-IG regression model is greater than the Negative Binomial regression model. It shows that P-IG regression model is better than Negative Binomial regression model.

Original languageEnglish
Article number012028
JournalJournal of Physics: Conference Series
Volume1442
Issue number1
DOIs
Publication statusPublished - 29 Jan 2020
EventBasic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017 - , Indonesia
Duration: 18 Aug 201719 Aug 2017

Keywords

  • goodness-of-fit statistics
  • negative binomial regression model
  • over dispersion
  • P-IG regression model

Fingerprint Dive into the research topics of 'Comparing Poisson-Inverse Gaussian Model and Negative Binomial Model on case study: Horseshoe crabs data'. Together they form a unique fingerprint.

  • Cite this