On Distributions of Runs in the Compound Binomial Risk Model

No Thumbnail Available

Date

2014

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

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

Events

Abstract

This paper is concerned with the distribution of runs associated with claim indicators in a compound binomial risk model. We study the total number of claims, the longest run without claim, the shortest run without claim and the total number of runs up to a fixed period before the occurrence of a ruin. These quantities are potentially useful for an investment strategy of an insurance company and for understanding the behavior of a specific portfolio over time. We obtain recursive equations for the exact distributions of these random variables. We also illustrate the theoretical results with numerical computations.

Description

Eryilmaz, Serkan/0000-0002-2108-1781
ORCID
Eryilmaz, Serkan ORCID logo

Keywords

Compound binomial model, Recurrence formula, Longest run, Shortest run

Turkish CoHE Thesis Center URL

Fields of Science

Citation

WoS Q

Q4

Scopus Q

Q3

Source

Volume

16

Issue

1

Start Page

149

End Page

159

URI

https://doi.org/10.1007/s11009-012-9303-x
 https://hdl.handle.net/20.500.14411/214

Collections

WOS
Scopus