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

Print

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

Embargo Period

4-15-2021

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