Zobrazeno 1 - 10
of 1 830
pro vyhledávání: '"A, Aneiros"'
Autor:
Novo, Silvia, Aneiros, Germán
Functional data analysis has become a tool of interest in applied areas such as economics, medicine, and chemistry. Among the techniques developed in recent literature, functional semiparametric regression stands out for its balance between flexible
Externí odkaz:
http://arxiv.org/abs/2405.14048
Publikováno v:
TEST, 2024
This paper focuses on a semiparametric regression model in which the response variable is explained by the sum of two components. One of them is parametric (linear), the corresponding explanatory variable is measured with additive error and its dimen
Externí odkaz:
http://arxiv.org/abs/2402.11292
Publikováno v:
Journal of Multivariate Analysis, 188:104871, 2022
Despite of various similar features, Functional Data Analysis and High-Dimensional Data Analysis are two major fields in Statistics that grew up recently almost independently one from each other. The aim of this paper is to propose a survey on method
Externí odkaz:
http://arxiv.org/abs/2401.14867
Publikováno v:
Australian and New Zealand Journal of Statistics, 63: 606-638, 2021
A new sparse semiparametric model is proposed, which incorporates the influence of two functional random variables in a scalar response in a flexible and interpretable manner. One of the functional covariates is included through a single-index struct
Externí odkaz:
http://arxiv.org/abs/2401.14864
Publikováno v:
Statistics & Probability Letters, 171:109028, 2021
A fast and flexible $k$NN procedure is developed for dealing with a semiparametric functional regression model involving both partial-linear and single-index components. Rates of uniform consistency are presented. Simulated experiments highlight the
Externí odkaz:
http://arxiv.org/abs/2401.14848
Publikováno v:
TEST, 30: 481-504, 2021
This paper aims to front with dimensionality reduction in regression setting when the predictors are a mixture of functional variable and high-dimensional vector. A flexible model, combining both sparse linear ideas together with semiparametrics, is
Externí odkaz:
http://arxiv.org/abs/2401.14841
Publikováno v:
Journal of Nonparametric Statistics, 31(2): 364-392, 2019
This paper develops a new automatic and location-adaptive procedure for estimating regression in a Functional Single-Index Model (FSIM). This procedure is based on $k$-Nearest Neighbours ($k$NN) ideas. The asymptotic study includes results for automa
Externí odkaz:
http://arxiv.org/abs/2401.14836
The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The firs
Externí odkaz:
http://arxiv.org/abs/2401.11885
Autor:
F. Bayona Gracia, I. Expósito Ruiz, I. Contreras Bustos, M. García Romero, E. Suárez Castro, A. Puy Núñez, E. Costa Arpín, I. López Dequidt, Á. Aneiros Díaz, M. Freijo Arce, J. Abella Corral
Publikováno v:
Neurology Perspectives, Vol 4, Iss , Pp 232- (2024)
Externí odkaz:
https://doaj.org/article/2fe0cc37e5a645e487db694183e53ebb
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 162, Iss , Pp 110244- (2024)
The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The firs
Externí odkaz:
https://doaj.org/article/056d3fc2fabd41c08f9974ed824240e1