Semiparametric principal component poisson regression on clustered data |
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Authors: | Kristina Celene M Manalaysay |
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Institution: | 1. School of Statistics, University of the Philippines Diliman, Quezon City, Philippines;2. InterContinental Hotels Group, Makati City, Philippines |
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Abstract: | In modeling count data with multivariate predictors, we often encounter problems with clustering of observations and interdependency of predictors. We propose to use principal components of predictors to mitigate the multicollinearity problem and to abate information losses due to dimension reduction, a semiparametric link between the count dependent variable and the principal components is postulated. Clustering of observations is accounted into the model as a random component and the model is estimated via the backfitting algorithm. Simulation study illustrates the advantages of the proposed model over standard poisson regression in a wide range of scenarios. |
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Keywords: | Clustered data Multicollinearity Principal components analysis Semiparametric poisson regression |
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