Automatic Generalized Nonparametric Regression via Maximum Likelihood
نویسنده
چکیده
A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation of the variance component in the model. However, multidimensional integrals arise in the likelihood. This paper describes and compares some approaches to this problem in the context of nonparametric regression.
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