We present a unified framework able to fit the entire quantile process, namely to estimate simultaneously multiple non-crossing quantile curves. The framework relies on assuming each regression parameter varies smoothly across the percentile direction according to B-splines whose coefficients obey proper restrictions. Multiple linear and penalized smooth terms are allowed and the corresponding tuning parameters are estimated efficiently as part of the model fitting. Monotonicity and concavity constraints on the smoothed relationships are also easily accounted for in the framework. Simulation results provide evidence our proposal exhibits good statistical performance with respect to competitors while guaranteeing the non-crossing property and modest computational load. Analyses on a real dataset related to vocabulary size growth are presented to illustrate the model capability in practice.

Muggeo, V., Sottile, G., Cilluffo, G. (2023). Joint modelling of non-crossing additive quantile regression via constrained B-spline varying coefficients. STATISTICAL MODELLING, 23(5-6), 540-554 [10.1177/1471082X231181734].

Joint modelling of non-crossing additive quantile regression via constrained B-spline varying coefficients

Muggeo, VMR
;
Sottile, G;Cilluffo, G
2023-01-01

Abstract

We present a unified framework able to fit the entire quantile process, namely to estimate simultaneously multiple non-crossing quantile curves. The framework relies on assuming each regression parameter varies smoothly across the percentile direction according to B-splines whose coefficients obey proper restrictions. Multiple linear and penalized smooth terms are allowed and the corresponding tuning parameters are estimated efficiently as part of the model fitting. Monotonicity and concavity constraints on the smoothed relationships are also easily accounted for in the framework. Simulation results provide evidence our proposal exhibits good statistical performance with respect to competitors while guaranteeing the non-crossing property and modest computational load. Analyses on a real dataset related to vocabulary size growth are presented to illustrate the model capability in practice.
2023
Muggeo, V., Sottile, G., Cilluffo, G. (2023). Joint modelling of non-crossing additive quantile regression via constrained B-spline varying coefficients. STATISTICAL MODELLING, 23(5-6), 540-554 [10.1177/1471082X231181734].
File in questo prodotto:
File Dimensione Formato  
muggeo-et-al-2023-joint-modelling-of-non-crossing-additive-quantile-regression-via-constrained-b-spline-varying.pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 670.75 kB
Formato Adobe PDF
670.75 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/616833
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact