Modelling Zero-Inflated Clustered Count Data: A Semiparametric Approach

Year: 2014       Vol.: 63       No.: 1      

Authors: Kevin Carl P. Santos

Abstract:

This paper proposes to use an additive semiparametric Poisson regression in modelling zero-inflated clustered data. Two estimation methods are exploited in this paper based on de Vera (2010). The first simultaneously estimates both the parametric and nonparametric parts of the model. The second utilizes the backfitting algorithm by smoothing the nonparametric function of the covariates and then estimating the parametric parts of the postulated model. The predictive accuracy, measured in terms of root mean square error (RMSE), of the proposed methods is compared to that of ordinary Zero-Inflated Poisson (ZIP) regression model. It is found out through simulation study that the average RMSE of the ordinary ZIP regression model is at most 81% and 27% higher for equal and unequal cluster sizes, respectively, than that of proposed model whose parametric and nonparametric parts are simultaneously estimated.

Keywords: Zero-Inflated Poisson models, clustered data, Generalized Additive Models, backfitting algorithm

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