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Abstract Details

Activity Number: 135
Type: Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #306375
Title: Inference for Zero-Inflated and Overdispersed Count-Data Models with Applications
Author(s): Santanu Chakraborty*+ and Sujay Datta
Companies: The University of Texas Pan American and University of Akron
Address: 1201 West University Drive, Edinburg, TX, 78541, United States
Keywords: Count-Data ; Zero-Inflated ; Overdispersed ; Poisson ; Negative Binomial/Multinomial ; Gene Exp and RNA-seq
Abstract:

Count data arise in numerous areas of application and can often be modeled by the Poisson distribution. However, in some cases (e.g. where there is a positive correlation among event occurrences) the observed variability in the data is higher than that specified by a Poisson model. In other cases, zero counts result from multiple sources necessitating an upward modification of P(X=0). To handle these situations, over-dispersed and zero-inflated Poisson distributions have been introduced or other discrete models (e.g. negative binomial and its zero-inflated version) have been proposed. Other applications have motivated a zero-inflated generalized Poisson model and a mixture of negative multinomial models. Here we present some key developments in frequentist and Bayesian parametric inference for those models, followed by some important examples of application including gene-expression data from transcription counting technologies (e.g. MPSS and SAGE), RNA-seq data, dental epidemiology data (DMFT index), geographic epidemiology data and animal breeding data. Results from simulated data and details on model-fitting will also be provided, time permitting.


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