Nonparametric hazard estimation under random censoring and dependent data

Abdelmalek Gagui

doi:10.3329/jsr.v59i2.88069

Nonparametric hazard estimation under random censoring and dependent data

Authors

Abdelmalek Gagui Laboratory of Pure and Applied Mathematics, University of Amar Telidji, Laghouat 03000, Algeria

DOI:

https://doi.org/10.3329/jsr.v59i2.88069

Keywords:

Functional data, local linear estimation, conditional hazard estimator, almost sure convergence, censored data, α-mixing dependeny

Abstract

This paper investigates the conditional hazard function of a scalar response variable given a predictor that takes values in a semi-metric space. We employ a local linear estimator for the conditional density and cumulative distribution function, and establish almost sure convergence rates under α-mixing dependence. The analysis is conducted under a set of regularity conditions for the proposed estimator. Furthermore, the practical relevance of the theoretical results is demonstrated through a simulation study.

Journal of Statistical Research 2025, Vol. 59, No. 2, pp. 259-278

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Published

2026-03-01

How to Cite

Gagui, A. (2026). Nonparametric hazard estimation under random censoring and dependent data. Journal of Statistical Research , 59(2), 259–278. https://doi.org/10.3329/jsr.v59i2.88069