Spatial Expectile Predictions for Elliptical Random Fields

Véronique Maume-Deschamps (1, 2) , Didier Rullière (3) , Antoine Usseglio-Carleve (2)

1 ICJ - Institut Camille Jordan
2 PSPM - Probabilités, statistique, physique mathématique
3 SAF - Laboratoire de Sciences Actuarielle et Financière

In this work, we consider an elliptical random field. We propose some spatial expectile predictions at one site given observations of the field at some other locations. To this aim, we first give exact expressions for conditional expectiles, and discuss problems that occur for computing these values. A first affine expectile regression predictor is detailed, an explicit iterative algorithm is obtained, and its distribution is given. Direct simple expressions are derived for some particular elliptical random fields. The performance of this expectile regression is shown to be very poor for extremal expectile levels, so that a second predictor is proposed. We prove that this new extremal prediction is asymptotically equivalent to the true conditional expectile. We also provide some numerical illustrations, and conclude that Expectile Regression may perform poorly when one leaves the Gaussian random field setting.

Mots clés: Extremal expectile; Elliptical distributions; Spatial prediction; Expectile regression; Kriging

Domaines: Statistiques [math.ST]; Probabilités [math.PR]; Statistiques [stat]

Véronique Maume-Deschamps, Didier Rullière, Antoine Usseglio-Carleve. Spatial Expectile Predictions for Elliptical Random Fields. Methodology and Computing in Applied Probability, 2018, 20 (2), pp.643-671. ⟨10.1007/s11009-017-9583-2⟩. ⟨hal-01399093v2⟩