Expectile regression for spatial functional data analysis (sFDA)

Rachdi, Mustapha; Laksaci, Ali; Al-Kandari, Noriah M.

doi:10.1007/s00184-021-00846-x

Cited by 5 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last reference, the weak and strong consistencies of the estimate together with almost-sure rates of convergence are established. For further asymptotic results on this operator, one can refer to [16,17], while, for other functional models such as the modal regression and/or the quantile regression, we refer to [18][19][20]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Functional Local Linear Asymmetric Least Square Regression Estimation: Application for Spatial Prediction of COVID-19 Propagation

Laksaci,

Bouzebda,

Alshahrani

et al. 2023

Symmetry

Self Cite

View full text Add to dashboard Cite

The problem of estimating the spatio-functional expectile regression for a given spatial mixing structure Xi,Yi∈F×R, when i∈ZN,N≥1 and F is a metric space, is investigated. We have proposed the M-estimation procedure to construct the Spatial Local Linear (SLL) estimator of the expectile regression function. The main contribution of this study is the establishment of the asymptotic properties of the SLL expectile regression estimator. Precisely, we establish the almost-complete convergence with rate. This result is proven under some mild conditions on the model in the mixing framework. The implementation of the SLL estimator is evaluated using an empirical investigation. A COVID-19 data application is performed, allowing this work to highlight the substantial superiority of the SLL-expectile over SLL-quantile in risk exploration.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatio-Functional Local Linear Asymmetric Least Square Regression Estimation: Application for Spatial Prediction of COVID-19 Propagation

Laksaci,

Bouzebda,

Alshahrani

et al. 2023

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

Asymptotic normality of the local linear estimator of the functional expectile regression

Litimein,

Laksaci,

Ait-Hennani

et al. 2024

Journal of Multivariate Analysis

View full text Add to dashboard Cite

Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data

et al. 2022

View full text Add to dashboard Cite

In this paper, we study the nonparametric estimation of the expected shortfall regression when the exogenous observation is functional. The constructed estimator is obtained by combining the double kernels estimator of both conditional value at risk and conditional density function. The asymptotic proprieties of this estimator are established under weak dependency condition. Precisely, we assume that the observations are generated from quasi-associated functional time series and we prove the almost complete convergence of the constructed estimator. This asymptotic result is obtained under a standard condition of functional time series analysis. The finite sample performance of this estimator is evaluated using artificial data.

show abstract

Expectile regression for spatial functional data analysis (sFDA)

Cited by 5 publications

References 32 publications

Spatio-Functional Local Linear Asymmetric Least Square Regression Estimation: Application for Spatial Prediction of COVID-19 Propagation

Spatio-Functional Local Linear Asymmetric Least Square Regression Estimation: Application for Spatial Prediction of COVID-19 Propagation

Asymptotic normality of the local linear estimator of the functional expectile regression

Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data

Contact Info

Product

Resources

About