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Carl Lee

Central Michigan University



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Felix Famoye

Central Michigan University



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Alfred Akinsete

Marshall University



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395 – Recent Advances in Zero-Inflated Regression Models

T-geometric Regression Models with Applications to Zero-Inflated Count Data

Sponsor: Biometrics Section
Keywords: discret analogue, generalized parametric models, under- and over-dispersion

Carl Lee

Central Michigan University

Felix Famoye

Central Michigan University

Alfred Akinsete

Marshall University

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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