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Thursday, May 17
Computational Statistics
Best of Computational and Graphical Statistics
Thu, May 17, 3:30 PM - 5:00 PM
Grand Ballroom E

Fused Lasso Additive Model (304344)


*Ashley Petersen, Division of Biostatistics, University of Minnesota 
Noah Simon, Dept. of Biostatistics, University of Washington 
Daniela Witten, Depts. of Biostatistics and Statistics, University of Washington 

Keywords: additive model, feature selection, high-dimensional, nonparametric regression

We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in the setting in which flexible and interpretable fits are desirable. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two data sets.