Multiple smoothing parameters selection in additive regression quantiles. (October 2021)

• Record Type: Journal Article
• Authors: Muggeo, Vito M.R.; Torretta, Federico; Eilers, Paul H. C.; Sciandra, Mariangela; Attanasio, Massimo
• Is Part Of: Statistical modelling. Volume 21:Number 5(2021)
• Page Start: 428
• Page End: 448
• Publication Date: 2021-10
• Subjects: additive quantile regression -- P-splines -- Schall algorithm -- Flexible modelling -- semiparametric quantile regression; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20929802
• Languages: English
• ISSNs: 1471-082X

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 coefficients as random effects from the symmetric Laplace distribution, and it turns out to be very efficient 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.