Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74450
Title: A Poisson-multinomial mixture approach to grouped and right-censored counts
Authors: Fu, Q
Guo, X 
Land, KC
Keywords: Grouped and right-censored count data
Mixed poisson models
MLE-GRC
Multinomial distribution
Zero-inflated Poisson distribution
Issue Date: 2018
Publisher: Marcel Dekker
Source: Communications in statistics. Theory and methods, 2018, v. 47, no. 2, p. 427-447 How to cite?
Journal: Communications in statistics. Theory and methods 
Abstract: Although count data are often collected in social, psychological, and epidemiological surveys in grouped and right-censored categories, there is a lack of statistical methods simultaneously taking both grouping and right-censoring into account. In this research, we propose a new generalized Poisson-multinomial mixture approach to model grouped and right-censored (GRC) count data. Based on a mixed Poisson-multinomial process for conceptualizing grouped and right-censored count data, we prove that the new maximum-likelihood estimator (MLE-GRC) is consistent and asymptotically normally distributed for both Poisson and zero-inflated Poisson models. The use of the MLE-GRC, implemented in an R function, is illustrated by both statistical simulation and empirical examples. This research provides a tool for epidemiologists to estimate incidence from grouped and right-censored count data and lays a foundation for regression analyses of such data structure.
URI: http://hdl.handle.net/10397/74450
ISSN: 0361-0926
DOI: 10.1080/03610926.2017.1303736
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
Citations as of Apr 6, 2019

Page view(s)

41
Last Week
3
Last month
Citations as of May 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.