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Automatic Smoothing for Poisson Regression

Abstract Adaptive choice of smoothing parameters for nonparametric Poisson regression (O'Sullivan et al., 1986) is considered in this article. A computable approximation of the unbiased risk estimate (AUBR) for Poisson regression is introduced. This approximation can be used to automatically tune the smoothing parameter for the penalized likelihood estimator. An alternative choice is the generalized approximate cross validation (GACV) proposed by Xiang and Wahba (1996). Although GACV enjoys a great success in practice when applying for nonparametric logisitic regression, its performance for Poisson regression is not clear. Numerical simulations have been conducted to evaluate the GACV and AUBR based tuning methods. We found that GACV has a tendency to oversmooth the data when the intensity function is small. As a consequence, we suggest tuning the smoothing parameter using AUBR in practice.