Recent advances in functional data analysis and high-dimensional statistics
Recent advances in functional data analysis and high-dimensional statistics - ScienceDirect
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Nonparametric modelling for functional data: selected survey and tracks for future
N Ling, P Vieu - Statistics, 2018 - Taylor & Francis
Nonparametric functional data analysis is a field whose development started some 15 years
ago and there is a very extensive literature on the topic (hundreds of papers published now) …
ago and there is a very extensive literature on the topic (hundreds of papers published now) …
On semiparametric regression in functional data analysis
N Ling, P Vieu - Wiley Interdisciplinary Reviews: Computational …, 2021 - Wiley Online Library
The aim of this paper is to provide a selected advanced review on semiparametric
regression which is an emergent promising field of researches in functional data analysis …
regression which is an emergent promising field of researches in functional data analysis …
Uniform consistency and uniform in number of neighbors consistency for nonparametric regression estimates and conditional U-statistics involving functional data
S Bouzebda, A Nezzal - Japanese Journal of Statistics and Data Science, 2022 - Springer
U-statistics represent a fundamental class of statistics arising from modeling quantities of
interest defined by multi-subject responses. U-statistics generalize the empirical mean of a …
interest defined by multi-subject responses. U-statistics generalize the empirical mean of a …
On functional data analysis and related topics
G Aneiros, I Horová, M Hušková, P Vieu - Journal of Multivariate Analysis, 2022 - Elsevier
This paper aims to present the various contributions to the Special Issue of the Journal of
Multivariate Analysis on Functional Data Analysis and some related topics including High …
Multivariate Analysis on Functional Data Analysis and some related topics including High …
Uniform limit theorems for a class of conditional Z-estimators when covariates are functions
S Bouzebda, M Chaouch - Journal of Multivariate Analysis, 2022 - Elsevier
This paper considers nonparametric estimation of a parameter, which is a zero of a certain
estimating equation, indexed by a class of functions and depending on an infinite …
estimating equation, indexed by a class of functions and depending on an infinite …
Machine learning algorithms used in PSE environments: A didactic approach and critical perspective
LF Fuentes-Cortés, A Flores-Tlacuahuac… - Industrial & …, 2022 - ACS Publications
This work addresses recent developments for solving problems in process systems
engineering based on machine learning algorithms. A general description of most popular …
engineering based on machine learning algorithms. A general description of most popular …
[HTML][HTML] Mode shift behaviour and user willingness to adopt the electric two-wheeler: A study based on Indian road user preferences
M Murugan, S Marisamynathan - International journal of transportation …, 2023 - Elsevier
As per statistics, two-wheeler (TW) alone shares the highest number of vehicle registrations
in India, which develops the various transportation-related issues such as traffic conflicts …
in India, which develops the various transportation-related issues such as traffic conflicts …
Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional U-statistics involving functional data
S Bouzebda, B Nemouchi - Journal of Nonparametric Statistics, 2020 - Taylor & Francis
ABSTRACT W. Stute [(1991), Annals of Probability, 19, 812–825] introduced a class of so-
called conditional U-statistics, which may be viewed as a generalisation of the Nadaraya …
called conditional U-statistics, which may be viewed as a generalisation of the Nadaraya …
The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data
M Mohammedi, S Bouzebda, A Laksaci - Journal of Multivariate Analysis, 2021 - Elsevier
The aim of this paper is to nonparametrically estimate the expectile regression in the case of
a functional predictor and a scalar response. More precisely, we construct a kernel-type …
a functional predictor and a scalar response. More precisely, we construct a kernel-type …