Nonparametric recursive method for moment generating function kernel-type estimators

被引：2
|
作者：
Bouzebda, Salim [1 ]
Slaoui, Yousri [2 ]
机构：
[1] Univ Technol Compiegne, Lab Math Appl Compiegne LMAC, Compiegne, France
[2] Univ Poitiers, Lab Math & Appl, Futuroscope Chasseneuil, Poitiers, France
来源：
STATISTICS & PROBABILITY LETTERS | 2022年 / 184卷
关键词：
Moment generating function; Kernel type estimator; Stochastic approximation algorithm; STOCHASTIC-APPROXIMATION METHOD; INTEGRATED SQUARED ERROR; NORMAL-DISTRIBUTIONS; DENSITY-FUNCTIONS; CONVERGENCE;
D O I：
10.1016/j.spl.2022.109422
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In the present paper, we are mainly concerned with the kernel type estimators for the moment generating function. More precisely, we establish the central limit theorem together with the characterization of the bias and the variance for the nonparametric recursive kernel-type estimators for the moment generating function under some mild conditions. Finally, we investigate the performance of the methodology for small samples through a short simulation study.(c) 2022 Elsevier B.V. All rights reserved.
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页数：11
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