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Title: Weighted average least squares for negative binomial regression Authors:  Kevin Huynh - University of Basel (Switzerland) [presenting]
Abstract: Model averaging methods have become an increasingly popular tool for improving predictions and dealing with model uncertainty, especially in Bayesian settings. Recently, frequentist model averaging methods, such as information theoretic and least squares model averaging, have emerged. The focus is on the issue of covariate uncertainty, where managing the computational resources is key: The model space grows exponentially with the number of covariates such that averaged models must often be approximated. Weighted average least squares (WALS), first introduced for (generalized) linear models in the econometric literature, combines Bayesian and frequentist aspects and additionally employs a semiorthogonal transformation of the regressors to reduce the computational burden. WALS is extended for generalized linear models to the negative binomial (NB) regression model for overdispersed count data. The predictive power of WALS for NB regression is compared to traditional estimators in a simulation experiment and in an empirical application using data on doctor visits.