Model fitting of zero-inflated and overdispersed count data
Date of Publication
2010
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Statistics Major in Actuarial Science
Subject Categories
Statistics and Probability
College
College of Science
Department/Unit
Mathematics and Statistics
Defense Panel Chair
Regina Ms. Tresvalles
Defense Panel Member
Kristine Mae M. Dela Cruz
Isagani B. Jos
Abstract/Summary
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in a Poisson or negative binominal model. This is referred to as zero-inflation. Additionally, data may display excess variability or overdispersion. Failure to model zero-inflation and overdispersion may lead to serious underestimation of standard errors and misleading regression parameter estimates. Poisson, negative binomial, zero-inflated Poison (ZIP) and zero-inflated negative binomial (ZINB) regression models are applied to CBMS Pasay City Poverty Census of 2005. Barangays are ranked according to estimated proportion of households below food poverty line. Overdispersion parameters indicate that the data is overdispersed and hence, a negative binomial model is preferred over Poisson model. However, zero-inflation parameters pose no significant evidence that the data is zero-inflated. Accordingly, goodness of fit statistics for the over-al best fit model show that the negative binomial regression model is the most preferred.
Abstract Format
html
Language
English
Format
Accession Number
TU16011
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
ix, 128 leaves, illustrations (some color), 28 cm.
Keywords
Regression analysis; Binomial distribution
Recommended Citation
Andan, J. S., & Cortez, A. P. (2010). Model fitting of zero-inflated and overdispersed count data. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/5335
Embargo Period
4-15-2021