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Spiegel, E ; Kneib, T.* ; Otto-Sobotka, F.*

Spatio-temporal expectile regression models.

Stat. Model. 20, 386-409 (2019)
Postprint DOI
Open Access Green
Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Expectile Regression ; Interaction Terms ; Main Effects ; Tensor Product ; P-spline ; Spatio ; Temporal Model; Generalized Additive-models; Confidence-intervals; Linear-models; Scale; Location; Splines
ISSN (print) / ISBN 1471-082X
e-ISSN 1477-0342
Zeitschrift Statistical Modelling
Quellenangaben Band: 20, Heft: 4, Seiten: 386-409 Artikelnummer: , Supplement: ,
Verlag Sage
Verlagsort 1 Olivers Yard, 55 City Road, London Ec1y 1sp, England
Begutachtungsstatus Peer reviewed