Multiple smoothing parameters selection in additive regression quantiles

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...

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Bibliographic Details
Published inStatistical modelling Vol. 21; no. 5; pp. 428 - 448
Main Authors Muggeo, Vito M.R., Torretta, Federico, Eilers, Paul H. C., Sciandra, Mariangela, Attanasio, Massimo
Format Journal Article
LanguageEnglish
Published New Delhi, India SAGE Publications 01.10.2021
Subjects
Schall algorithm
P-splines
Flexible modelling
semiparametric quantile regression
additive quantile regression
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Summary: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.
ISSN:1471-082X
1477-0342
DOI:10.1177/1471082X20929802