On the nonparametric estimation of the conditional hazard estimator in a single functional index

Artikel

This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good com- promise between nonparametric and parametric models. The principle aim is to prove the asymptotic normality of the proposed estimator under general conditions and in cases where the variables satisfy the strong mixing dependency. This was achieved by means of the kernel estimator method, based on a single-index structure. Finally, a simulation of our methodol- ogy shows that it is efficient for large sample sizes.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Statistics in Transition new series (SiTns) ; ISSN: 2450-0291 ; Volume: 23 ; Year: 2022 ; Issue: 2 ; Pages: 89-105 ; Warsaw: Sciendo

Classification and Topics

• Subject: single functional index ; conditional hazard function ; nonparametric estimation,»-mixing dependency ; asymptotic normality ; functional data

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gagui, Abdelmalek ; Chouaf, Abdelhak
• Event: Veröffentlichung
• (who): Sciendo
• (where): Warsaw
• (when): 2022

Further information

• DOI: doi:10.2478/stattrans-2022-0018
• Handle: http://hdl.handle.net/10419/266309
• Last update: 10.03.2025, 11:44 AM CET