6533b82cfe1ef96bd128f4c3

RESEARCH PRODUCT

Multiple smoothing parameters selection in additive regression quantiles

Massimo AttanasioFederico TorrettaMariangela SciandraPaul H. C. EilersVito M. R. Muggeo

subject

Statistics and ProbabilityIterative methodSchall algorithmexible modellingMathematicsofComputing_NUMERICALANALYSISAdditive quantile regression030229 sport sciencesP splines01 natural sciencesRegressionQuantile regression010104 statistics & probability03 medical and health sciences0302 clinical medicineP-splineStatisticsCovariatesemiparametric quantile regression0101 mathematicsStatistics Probability and UncertaintySmoothingSelection (genetic algorithm)QuantileMathematics

description

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.

yearjournalcountryeditionlanguage
2021-10-01Statistical Modelling
https://doi.org/10.1177/1471082x20929802