Nonparametric Recursive Kernel Type Eestimators for the Moment Generating Function Under Censored Data

Statistics, Optimization and Information Computing

article

Salim Bouzebda¹  Issam Elhattab¹  Yousri Slaoui Slaoui²  NourElhouda Taachouche¹ 

[1] LMAC, Université de Technologie de Compiégne;université de Poitiers

关键词: Moment generating function;    Kernel type estimator;    Stochastic approximation algorithm;    Censored data;   

DOI  :  10.19139/soic-2310-5070-1678

来源: Istituto Superiore di Sanita

【 摘 要 】

We are mainly concerned with kernel-type estimators for the moment-generating function in the present paper. More precisely, we establish the central limit theorem 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 in the censored data setting. Finally, we investigate the methodology’s performance for small samples through a short simulation study.

【 授权许可】

Unknown