Nonparametric estimation of expectile regression in functional dependent data
From MaRDI portal
Publication:5030947
DOI10.1080/10485252.2022.2027412OpenAlexW4210454170MaRDI QIDQ5030947
Salim Bouzebda, Ibrahim M. Almanjahie, Ali Laksaci, Zoulikha Kaid
Publication date: 18 February 2022
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485252.2022.2027412
kernel methodfinancial time seriessmall ball probabilityfunctional data analysis (FDA)functional time seriesalmost complete (a.co.) convergenceconditional expectile
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Nonparametric hypothesis testing (62G10) Estimation in multivariate analysis (62H12) Functional data analysis (62R10) Nonparametric robustness (62G35) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32)
Related Items (4)
Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds ⋮ On the variable bandwidth kernel estimation of conditional \(U\)-statistics at optimal rates in sup-norm ⋮ Weak-convergence of empirical conditional processes and conditional \(U\)-processes involving functional mixing data ⋮ Local linear estimate of the functional expectile regression
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Asymmetric Least Squares Estimation and Testing
- An introduction to recent advances in high/infinite dimensional statistics
- Assessing value at risk with CARE, the conditional autoregressive expectile models
- Bootstrap confidence intervals in functional nonparametric regression under dependence
- Asymptotic results for an \(L^1\)-norm kernel estimator of the conditional quantile for functional dependent data with application to climatology
- A partial overview of the theory of statistics with functional data
- High-dimensional generalizations of asymmetric least squares regression and their applications
- Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties
- Learning rates for kernel-based expectile regression
- Quantile regression and its empirical likelihood with missing response at random
- Expectiles and \(M\)-quantiles are quantiles
- Linear processes in function spaces. Theory and applications
- Expectile regression for analyzing heteroscedasticity in high dimension
- Semi-functional partial linear quantile regression
- Recent advances in functional data analysis and high-dimensional statistics
- Generalized autoregressive conditional heteroscedasticity
- The functional central limit theorem under the strong mixing condition
- Functional nonparametric model for time series: a fractal approach for dimension reduction
- Relating quantiles and expectiles under weighted-symmetry
- Curse of dimensionality and related issues in nonparametric functional regression
- Quantile regression and variable selection for partially linear model with randomly truncated data
- Weighted quantile regression for censored data with application to export duration data
- Missing responses at random in functional single index model for time series data
- Convergence rate of kernel regression estimation for time series data when both response and covariate are functional
- Functional single-index quantile regression models
- Tail expectile process and risk assessment
- Convergence rates for kernel regression in infinite-dimensional spaces
- Backtesting VaR and expectiles with realized scores
- Limiting law results for a class of conditional mode estimates for functional stationary ergodic data
- Nonparametric regression estimation for dependent functional data: asymptotic normality
- Nonparametric functional data analysis. Theory and practice.
- The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Nonparametric regression expectiles∗
- Hazard function given a functional variable: Non-parametric estimation under strong mixing conditions
- NonparametricM-quantile regression using penalised splines
- M-quantiles
- Regression Quantiles
- Nonparametric Estimation of the Mode of A Distribution of Random Curves
- Nonparametric modelling for functional data: selected survey and tracks for future
- The kernel regression estimation for randomly censored functional stationary ergodic data
- Estimation of Tail Risk Based on Extreme Expectiles
- Beyond mean regression
- Discussion: The beauty of expectiles
- Discussion: Living beyond our means
- Expectile and quantile regression—David and Goliath?
- FUNCTIONAL ADDITIVE QUANTILE REGRESSION
- Extremiles: A New Perspective on Asymmetric Least Squares
- NONPARAMETRIC REGRESSION ON FUNCTIONAL DATA: INFERENCE AND PRACTICAL ASPECTS
- Uniform consistency rate of kNN regression estimation for functional time series data
This page was built for publication: Nonparametric estimation of expectile regression in functional dependent data
