An introduction to semiparametric function-on-scalar regression. (June 2018)

• Record Type: Journal Article
• Authors: Bauer, Alexander; Scheipl, Fabian; Küchenhoff, Helmut; Gabriel, Alice-Agnes
• Other Names: Groll Andreas guest-editor.; Kneib Thomas guest-editor.; Mayr Andreas guest-editor.
• Is Part Of: Statistical modelling. Volume 18:Number 3/4(2018)
• Page Start: 346
• Page End: 364
• Publication Date: 2018-06
• Subjects: Functional regression -- Functional response -- generalized additive model -- semiparametric regression -- penalized splines -- geophysics; 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: 10.1177/1471082X17748034
• Languages: English
• ISSNs: 1471-082X

Abstract

Abstract: Function-on-scalar regression models feature a function over some domain as the response while the regressors are scalars. Collections of time series as well as 2D or 3D images can be considered as functional responses. We provide a hands-on introduction for a flexible semiparametric approach for function-on-scalar regression, using spatially referenced time series of ground velocity measurements from large-scale simulated earthquake data as a running example. We discuss important practical considerations and challenges in the modelling process and outline best practices. The outline of our approach is complemented by comprehensiveR code, freely available in the online appendix. This text is aimed at analysts with a working knowledge of generalized regression models and penalized splines.