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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/45381
Title: Bayesian Predictive Inference for Zero-Inflated Poisson (ZIP) Distribution with Applications
Authors: Suntaree Unhapipat
Montip Tiensuwan
Nabendu Pal
University of Louisiana at Lafayette
Mahidol University
Commission on Higher Education
Keywords: Business, Management and Accounting;Mathematics
Issue Date: 2-Jan-2018
Citation: American Journal of Mathematical and Management Sciences. Vol.37, No.1 (2018), 66-79
Abstract: © 2018 Taylor & Francis Group, LLC. SYNOPTIC ABSTRACT: Count data (over time or space) that has an unusually large number of zeros cannot be modelled by the usual Poisson distribution. Typically, a better fit to such data is provided by a two parameter zero-inflated Poisson (ZIP) distribution. This work deals with Bayesian predictive inference under the ZIP model where various types of prior distributions have been considered. The applicability and usefulness of our proposed Bayesian techniques under the ZIP model have been demonstrated by four examples with real-life datasets, ranging from public health to natural disasters to vehicle accidents.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85031810113&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/45381
ISSN: 01966324
Appears in Collections:Scopus 2018

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