Expectile regression for spatial functional data analysis (sFDA)

Mustapha Rachdi (), Ali Laksaci and Noriah M. Al-Kandari
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Mustapha Rachdi: Univ. Grenoble Alpes
Ali Laksaci: King Khalid University
Noriah M. Al-Kandari: Kuwait University

Metrika: International Journal for Theoretical and Applied Statistics, 2022, vol. 85, issue 5, No 5, 627-655

Abstract: Abstract This paper deals with the nonparametric estimation of the expectile regression when the observations are spatially correlated and are of a functional nature. The main findings of this work is the establishment of the almost complete convergence for the proposed estimator under some general mixing conditions. The performance of the proposed estimator is examined by using simulated data. Finally, the studied model is used to evaluate the air quality indicators in northeast China.

Keywords: Spatio-functional data analysis (sFDA); Expected Shortfall (ES); Value-at-Risk (VaR); Expectile regression; Risk measures; ARCH process (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00184-021-00846-x

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