A0365
Title: Estimation and selection for spatial zero-inflated count models
Authors: Chun-Shu Chen - National Central University (Taiwan) [presenting]
Chung-Wei Shen - National Chung Cheng University (Taiwan)
Abstract: The count data arise in many scientific areas. The focus is on spatial count responses with an excessive number of zeros and a set of available covariates. How to estimate model parameters, as well as a selection of important covariates for spatial zero-inflated count models, are both essential. Importantly, to alleviate deviations from model assumptions, a spatial zero-inflated Poisson-like methodology is proposed to model this type of data, which just relies on assumptions for the first two moments of spatial count responses. An effectively iterative estimation procedure is then designed between the generalized estimating equation and the weighted least squares method to estimate the regression coefficients and the variogram of the data model, respectively. Moreover, the stabilization of estimators is evaluated via a block jackknife technique. Further, a distribution-free model selection criterion based on an estimate of the mean squared error of the estimated mean structure is proposed to select the best subset of covariates. Numerical results show the effectiveness of the proposed methods.
