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Activity Number:
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395
- Recent Advances in Zero-Inflated Regression Models
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #322619
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Title:
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T-Geometric Regression Models with Applications to Zero-Inflated Count Data
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Author(s):
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Carl Lee* and Felix Famoye and Alfred Akinsete
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Companies:
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Central Michigan Univ and Central Michigan University and Marshall University
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Keywords:
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discret analogue ;
generalized parametric models ;
under- and over-dispersion
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Abstract:
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A method of developing generalized parametric regression models for modeling count data is proposed and studied. The method is based on the framework of the T-geometric family of distributions. A T-geometric distribution is the discrete analogue of the corresponding continuous distribution. The general methodology is applied to derive several generalized regression models for count data. These regression models can fit count data with under-dispersion or over-dispersion. The extension to model truncated or zero inflated data is addressed. Some new generalized T-geometric regression models are applied to real world data sets to illustrate the flexibility of these models.
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Authors who are presenting talks have a * after their name.
