Treffer: Nonparametric recursive method for moment generating function kernel-type estimators
Title:
Nonparametric recursive method for moment generating function kernel-type estimators
Authors:
Contributors:
Laboratoire de Mathématiques Appliquées de Compiègne (LMAC), Université de Technologie de Compiègne (UTC), Laboratoire de mathématiques et applications UMR 7348 (LMA Poitiers ), Université de Poitiers = University of Poitiers (UP)-Centre National de la Recherche Scientifique (CNRS)
Source:
ISSN: 0167-7152 ; Statistics and Probability Letters ; https://univ-poitiers.hal.science/hal-04389629 ; Statistics and Probability Letters, 2022, 184, pp.109422. ⟨10.1016/j.spl.2022.109422⟩.
Publisher Information:
CCSD
Elsevier
Elsevier
Publication Year:
2022
Collection:
Université de Technologie de Compiègne: HAL
Subject Terms:
Document Type:
Fachzeitschrift
article in journal/newspaper
Language:
English
DOI:
10.1016/j.spl.2022.109422
Availability:
Rights:
http://creativecommons.org/licenses/by-nc/ ; info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.19A0BEC0
Database:
BASE
Weitere Informationen
International audience ; n 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.