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

  • Salim Bouzebda LMAC, Université de Technologie de Compiégne, France
  • Issam Elhattab LMAC, Université de Technologie de Compiégne, France
  • Yousri Slaoui Slaoui université de Poitiers
  • NourElhouda Taachouche LMAC, Université de Technologie de Compiégne, France
Keywords: Moment generating function, Kernel type estimator, Stochastic approximation algorithm, Censored data

Abstract

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.

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Published
2023-02-17
How to Cite
Bouzebda, S., Elhattab, I., Slaoui, Y. S., & Taachouche, N. (2023). Nonparametric Recursive Kernel Type Eestimators for the Moment Generating Function Under Censored Data. Statistics, Optimization & Information Computing, 11(2), 196-215. https://doi.org/10.19139/soic-2310-5070-1678
Section
Research Articles