Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments

Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments

Bibliographic Details
Main Author: Fan, Huihao
Language:English
Published: University of Cincinnati / OhioLINK 2014
Subjects:
Biostatistics
over-dispersion
count data
Poisson distribution
zero-inflation
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin14074045132021-08-03T06:26:57Z Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments Fan, Huihao Biostatistics over-dispersion count data Poisson distribution zero-inflation Real-life count data are frequently characterized by over-dispersion (variance greater than mean) and excess zeros. Various methods exist in literature to combat zero-inflation and over-dispersion in count data. Among them Zero-inflated count models provide a parsimonious yet powerful way to model excess zeros in addition to allowing for over-dispersion. Such models assume that the counts are a mixture of two separate data generation process: one generates only zeros, and the other is a Poisson type data-generating process. Among mostly discussed models are zero-inflated Poisson (ZIP), zero inflated negative binomial (ZINB) and zero-inflated generalized Poisson (ZIGP). However, the performance and application condition of these models are not thoroughly studied. In this work, these common zero-inflation models are reviewed and compared under specified over-dispersion conditions via simulated data and real-life data in terms of statistical power and type I error rate. Performance of each model will be listed side by side to give a clear view of each model’s pros and cons in specific over-dispersion and zero-inflation condition. Further, the ZIGP model is chosen to extend to a more general situation where a random effect is incorporated to account for within-subject correlation and between subject heterogeneity. Likelihood based estimation of treatment effect will be developed for analysis of randomized experiments with random effect. Effect of model misspecification on model’s performance will be investigated in areas such as type I error rate, standard error and empirical statistical power. Case studies will be presented to illustrate the application these models. 2014-09-12 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biostatistics
over-dispersion
count data
Poisson distribution
zero-inflation
spellingShingle Biostatistics
over-dispersion
count data
Poisson distribution
zero-inflation
Fan, Huihao
Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
author Fan, Huihao
author_facet Fan, Huihao
author_sort Fan, Huihao
title Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
title_short Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
title_full Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
title_fullStr Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
title_full_unstemmed Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments
title_sort test of treatment effect with zero-inflated over-dispersed count data from randomized single factor experiments
publisher University of Cincinnati / OhioLINK
publishDate 2014
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513
work_keys_str_mv AT fanhuihao testoftreatmenteffectwithzeroinflatedoverdispersedcountdatafromrandomizedsinglefactorexperiments
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