Open AccessGeometric Regression for Modelling Count Data on the Time-to-First Antenatal Care Visit
Author(s) -
Zainab Mohammed Darwish Al-Balushi,
AUTHOR_ID,
M. Mazharul Islam,
AUTHOR_ID
Publication year - 2020
Publication title -
journal of statistics : advances in theory and applications
Language(s) - English
Resource type - Journals
ISSN - 0975-1262
DOI - 10.18642/jsata_7100122148
Subject(s) - count data , negative binomial distribution , geometric distribution , poisson regression , overdispersion , poisson distribution , quasi likelihood , statistics , generalized linear model , regression analysis , mathematics , dispersion (optics) , geometric design , binomial distribution , event (particle physics) , binomial regression , probability distribution , medicine , population , geometry , physics , environmental health , quantum mechanics , optics
Geometric distribution belongs to the family of discrete distribution that deals with the count of trail needed for first occurrence or success of any event. However, little attention has been paid in applying the GLM for the geometric distribution, which has a very simple form for its probability mass function with a single parameter. In this study, an attempt has been made to introduce geometric regression for modelling the count data. We have illustrated the suitability of the geometric regression model for analyzing the count data on time to first antenatal care visit that displayed under-dispersion, and the results were compared with Poisson and negative binomial regressions. We conclude that the geometric regression model may provide a flexible model for fitting count data sets which may present over-dispersion or under-dispersion, and the model may serve as an alternative model to the very familiar Poisson and negative binomial models for modelling count data.