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Activity Number: 50
Type: Invited
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #314356
Title: Fused Lasso Additive Model
Author(s): Ashley Petersen and Daniela Witten* and Noah Simon
Companies: University of Washington and University of Washington and University of Washington
Keywords: interpretable ; high-dimensional ; additive ; prediction ; sparsity

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 the 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.

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