The strong consistency and asymptotic normality of the kernel estimator type in functional single index model in presence of censored data

The strong consistency and asymptotic normality of the kernel estimator type in functional single index model in presence of censored data

In the present study, we address the nonparametric estimation challenge related to the regression function within the Single Functional Index Model in the random censoring framework. The principal achievement of this investigation lies in the establishment of the asymptotic characteristics of the es...

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Hlavní autoři: Said Attaoui, Billal Bentata, Salim Bouzebda, Ali Laksaci
Médium: Článek
Jazyk:English
Vydáno: AIMS Press 2024-02-01
Edice:AIMS Mathematics
Témata:
almost complete convergence
convergence rates
exponential inequality
kernel regression
On-line přístup:https://www.aimspress.com/article/doi/10.3934/math.2024356?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/math.2024356?viewType=HTML

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