Work

Limit Theorems for Kernel Regression Estimator for Quasi-Associated Functional Censored Time Series Within Single Index Structure

Year: 2025

Type: article

Abstract: In this paper, we develop kernel-based estimators for regression functions under a functional single-index model, applied to censored time series data. By capitalizing on the single-index structure, we reduce the dimensionality of the covariate-response relationship, thereby preserving the ability to capture intricate dependencies while maintaining a relatively parsimonious form. Specifically, our framework utilizes nonparametric kernel estimation within a quasi-association setting to characterize the underlying relationships. Under mild regularity conditions, we demonstrate that these estimators attain both strong uniform consistency and asymptotic normality. Through extensive simulation experiments, we confirm their robust finite-sample performance. Moreover, an empirical examination using intraday Nikkei stock index returns illustrates that the proposed method significantly outperforms traditional nonparametric regression approaches.

Source: Mathematics

DOI: 10.3390/math13050886

Authors: Said Attaoui, Oum Elkheir Benouda, Salim Bouzebda, Ali Laksaci

Institutions: Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, Université de Technologie de Compiègne, Laboratoire de mathématiques appliquées de Compiègne, King Khalid University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Université des Sciences et de la Technologie d'Oran Mohamed-Boudiaf