Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/266309 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Statistics in Transition new series (SiTns) [ISSN:] 2450-0291 [Volume:] 23 [Issue:] 2 [Publisher:] Sciendo [Place:] Warsaw [Year:] 2022 [Pages:] 89-105
Verlag: 
Sciendo, Warsaw
Zusammenfassung: 
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.
Schlagwörter: 
single functional index
conditional hazard function
nonparametric estimation,»-mixing dependency
asymptotic normality
functional data
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