Meta M. Voelker

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We describe Likelihood Basis Pursuit, a nonparametric method for variable selection and model building, based on merging ideas from Lasso and Basis Pursuit works and from smoothing spline ANOVA models. An application to nonparametric variable selection for risk factor modeling in the Wisconsin Epidemiological Study of Diabetic Retinopathy is described.(More)
Abstract This paper presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tensor product reproducing kernel Hilbert space, we decompose the log likelihood into the sum of different functional components such as main effects and interactions, with each(More)
We consider a non-parametric penalized likelihood approach for model building called likelihood basis pursuit (LBP) that determines the probabilities of binary outcomes given explanatory vectors while automatically selecting important features. The LBP model involves parameters that balance the competing goals of maximizing the log-likelihood and minimizing(More)
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