Work

Limit Theorem for Kernel Estimate of the Conditional Hazard Function with Weakly Dependent Functional Data

Year: 2025

Type: article

Abstract: This paper examines the asymptotic behavior of the conditional hazard function using kernel-based methods, with particular emphasis on functional weakly dependent data. In particular, we establish the asymptotic normality of the proposed estimator when the covariate follows a functional quasi-associated process. This contribution extends the scope of nonparametric inference under weak dependence within the framework of functional data analysis. The estimator is constructed through kernel smoothing techniques inspired by the classical Nadaraya–Watson approach, and its theoretical properties are rigorously derived under appropriate regularity conditions. To evaluate its practical performance, we carried out an extensive simulation study, where finite-sample outcomes were compared with their asymptotic counterparts. The results showed the robustness and reliability of the estimator across a range of scenarios, thereby confirming the validity of the proposed limit theorem in empirical settings.

Source: Symmetry

DOI: 10.3390/sym17101777

Authors: Abderrahmane Belguerna, Abdelkader Rassoul, Hamza Daoudi, Zouaoui Chikr Elmezouar, Fatimah Alshahrani

Institutions: Centre universitaire Salhi Ahmed - Naama, King Khalid University, Princess Nourah bint Abdulrahman University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Princess Nourah Bint Abdulrahman University, King Khalid University