Online Program

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Friday, May 31
Machine Learning
Recent Developments on Machine Learning
Fri, May 31, 10:30 AM - 12:05 PM
Regency Ballroom AB

P-Splines with an L1 Penalty for Repeated Measures (305036)

Thomas Braun, University of Michigan 
Michael R. Elliott, University of Michigan 
*Hui Jiang, University of Michigan 
Brian D. Segal, University of Michigan 

Keywords: Additive models, semiparametric regression, clustered data

P-splines are penalized B-splines, in which finite order differences in coefficients are typically penalized with an L2 norm. P-splines can be used for semiparametric regression and can include random effects to account for within-subject correlations. In addition to L2 penalties, L1-type penalties have been used in nonparametric and semiparametric regression to achieve greater flexibility, such as in locally adaptive regression splines, L1 trend filtering, and the fused lasso additive model. However, there has been less focus on using L1 penalties in P-splines, particularly for estimating conditional means. We demonstrate the potential benefits of using an L1 penalty in P-splines with an emphasis on fitting non-smooth functions. We propose an estimation procedure using the alternating direction method of multipliers and cross validation, and provide degrees of freedom and approximate confidence bands based on a ridge approximation to the L1 penalized fit. We also demonstrate potential uses through simulations and an application to electrodermal activity data collected as part of a stress study.