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• W2898834367 abstract "P-splines are penalized B-splines, in which finite order differences in coefficients are typically penalized with an $ell_2$ norm. P-splines can be used for semiparametric regression and can include random effects to account for within-subject variability. In addition to $ell_2$ penalties, $ell_1$-type penalties have been used in nonparametric and semiparametric regression to achieve greater flexibility, such as in locally adaptive regression splines, $ell_1$ trend filtering, and the fused lasso additive model. However, there has been less focus on using $ell_1$ penalties in P-splines, particularly for estimating conditional means. In this paper, we demonstrate the potential benefits of using an $ell_1$ 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 $ell_1$ penalized fit. We also demonstrate potential uses through simulations and an application to electrodermal activity data collected as part of a stress study." @default.
• W2898834367 created "2018-11-09" @default.
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• W2898834367 date "2018-01-01" @default.
• W2898834367 modified "2023-09-30" @default.
• W2898834367 title "P-splines with an $ell_{1}$ penalty for repeated measures" @default.
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• W2898834367 doi "https://doi.org/10.1214/18-ejs1487" @default.
• W2898834367 hasPublicationYear "2018" @default.
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