Treffer: Nonparametric Recursive Kernel Type Eestimators for the Moment Generating Function Under Censored Data
Title:
Nonparametric Recursive Kernel Type Eestimators for the Moment Generating Function Under Censored Data
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), GDR 3477 GeoSto
Source:
ISSN: 2311-004X.
Publisher Information:
CCSD
International Academic Press
International Academic Press
Publication Year:
2023
Collection:
Université de Technologie de Compiègne: HAL
Subject Terms:
Document Type:
Fachzeitschrift
article in journal/newspaper
Language:
English
DOI:
10.19139/soic-2310-5070-1678
Availability:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.E6BCA375
Database:
BASE
Weitere Informationen
International audience ; 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.