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Activity Number: 591
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #315924
Title: A Quasi-Likelihood Approach to Zero-Inflated Spatial Count Data
Author(s): Anthea Monod*
Companies: Duke University
Keywords: Generalized linear models ; Generalized estimating equations ; Zero-inflated Poisson models ; Spatial analysis ; Marked point processes
Abstract:

In the framework of generalized linear models, we develop a zero-inflated Poisson regression model, which explains the variability of the responses given a set of covariates, and additionally allows for the distinction of two kinds of zeros: sampling ("bad luck" zeros), and structural (zeros due to the data-generating process). We adapt this model to the spatial setting by incorporating dependence via a quasi-likelihood strategy, which provides consistent, efficient and asymptotically normal estimators, even under erroneous assumptions of the covariance structure, which also overcomes the need for the complete specification of a probability model.

We additionally propose methods for the simulation of zero-inflated spatial stochastic processes. This is done by deconstructing the entire process into a mixed, marked spatial point process: we augment existing algorithms for the simulation of spatial marked point processes to comprise a stochastic mechanism to generate zero-abundant marks (counts) at each location. We propose several such mechanisms, and consider interaction and dependence processes for random locations as well as over a lattice.


Authors who are presenting talks have a * after their name.

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