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EnglishWe propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coeffcients as random effects from the symmetric Laplace distribution and it turns out to be very ecient and particularly attractive with multiple smooth terms. Through simulations we compare our proposal with some alternative approaches, including the traditional ones based on minimization of the Schwarz Information Criterion. A real-data analysis is presented to illustrate the method in practice.
Vito M.R. Muggeo, Federico Torretta, Paul H. C. Eilers, Mariangela Sciandra, & Massimo Attanasio (2020). Multiple smoothing parameters selection in additive regression quantiles. STATISTICAL MODELLING.
| Data di pubblicazione: | 2020 | |
| Titolo: | Multiple smoothing parameters selection in additive regression quantiles | |
| Autori: | ||
| Citazione: | Vito M.R. Muggeo, Federico Torretta, Paul H. C. Eilers, Mariangela Sciandra, & Massimo Attanasio (2020). Multiple smoothing parameters selection in additive regression quantiles. STATISTICAL MODELLING. | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1177/1471082X20929802 | |
| Abstract: | We propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coeffcients as random effects from the symmetric Laplace distribution and it turns out to be very ecient and particularly attractive with multiple smooth terms. Through simulations we compare our proposal with some alternative approaches, including the traditional ones based on minimization of the Schwarz Information Criterion. A real-data analysis is presented to illustrate the method in practice. | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/10447/414301
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