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Abstract Details
Activity Number:
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314
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #300698 |
Title:
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Modeling Zero-Inflated Continuous Data with Varying Dispersion
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Author(s):
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Ka Yui Karl Wu*+ and Wai Keung Li
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Companies:
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University of Hong Kong and University of Hong Kong
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Address:
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Department of Statistics & Actuarial Science, Hong Kong, International, , China
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Keywords:
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EM Algorithm ;
Generalized Linear Model ;
Overdispersion
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Abstract:
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Zero-inflated data are often observed in empirical studies of different scientific fields. Data are considered as zero-inflated if the observed values of a random vector contain significantly more zeros than expected. Excessive occurred zeros to the dependent variable in a regression model discourage straightforward modelling by classical regression techniques. In the past, zero-inflation is considered as a count data problem and Zero-Inflated Poisson regression (ZIP) has been established to be the standard tool for zero-inflation modelling. The approach is based on a joint probability density function in which the probability for non-zero observations and response mean are both parameters and interlinked by two pseudo-simultaneously estimated linear models. However, constant dispersion is often assumed even when overdispersion is a common feature in almost every empirical data set. In our paper, the dispersion is formulated as a gamma generalized submodel interlinked with a mean and a zero-inflation probability submodel. We propose a modified triple, nested iterative approach to model response mean, dispersion and zero-inflation probability simultaneously.
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