Estimating the Parameter of a Geometric Distribution From Series System Data

No Thumbnail Available

Date

2024

Authors

Eryilmaz, Serkan
Eryılmaz, Serkan
Kateri, Maria

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Thumbnail Image
Organizational Unit
Industrial Engineering
(1998)
Industrial Engineering is a field of engineering that develops and applies methods and techniques to design, implement, develop and improve systems comprising of humans, materials, machines, energy and funding. Our department was founded in 1998, and since then, has graduated hundreds of individuals who may compete nationally and internationally into professional life. Accredited by MÜDEK in 2014, our student-centered education continues. In addition to acquiring the knowledge necessary for every Industrial engineer, our students are able to gain professional experience in their desired fields of expertise with a wide array of elective courses, such as E-commerce and ERP, Reliability, Tabulation, or Industrial Engineering Applications in the Energy Sector. With dissertation projects fictionalized on solving real problems at real companies, our students gain experience in the sector, and a wide network of contacts. Our education is supported with ERASMUS programs. With the scientific studies of our competent academic staff published in internationally-renowned magazines, our department ranks with the bests among other universities. IESC, one of the most active student networks at our university, continues to organize extensive, and productive events every year.

Journal Issue

Abstract

In a traditional setup of estimation of an unknown parameter of component lifetime distribution, system's continuous lifetime data is used. In this paper, we propose a simple and competitive estimator that is based on discrete lifetime data, i.e., the number of failed components at the time when the system fails. In particular, we consider the estimation of the parameter of a geometric distribution based on the system's lifetime data, and the number of failed components upon the failure of the system when the system has a series structure. Two moment estimators that are based on the system lifetime data and the number of failed components at the moment of system failure are obtained and their performances are compared in terms of the mean square error. The associated Bayesian estimators with non -informative priors are also discussed.

Description

Keywords

Geometric distribution, Moment estimator, Reliability, Series system, Bayesian estimation, Conjugate prior

Turkish CoHE Thesis Center URL

Fields of Science

Citation

0

WoS Q

Q1

Scopus Q

Source

Volume

450

Issue

Start Page

End Page

URI

https://doi.org/10.1016/j.cam.2024.115991
 https://hdl.handle.net/20.500.14411/48

Collections

WOS
Load more