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Test of Treatment Effect with Zero-I...

Fan, Huihao.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments./
作者:	Fan, Huihao.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:	Dissertation Abstracts International76-02B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3639089
ISBN:	9781321233209

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

Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments. - 126 p.

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2014.

This item must not be sold to any third party vendors.

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.

ISBN: 9781321233209Subjects--Topical Terms:

1002712
Biostatistics.

Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments.
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245 1 0 $a Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
500 $a Adviser: Marepalli Rao.
502 $a Thesis (Ph.D.)--University of Cincinnati, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
520 $a 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.
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710 2 $a University of Cincinnati. $b Biostatistics (Environmental Health). $3 2122881
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