Research Article

Nonparametric estimation of a renewal function in the case of censored sample

Volume: 9 Number: 2 December 27, 2019
EN

Nonparametric estimation of a renewal function in the case of censored sample

Abstract

A renewal process is a counting process which counts the number of renewals that occurs as a function of time, wherein the durations between successive renewals are random variables independent of one another, with identical F distributions. The mean value function data is frequently needed in applications of renewal processes. For the renewal function, open expressions depending on distribution function F can be calculated from each other. However, even though the distribution function F is known, the renewal function cannot be obtained analytically except for a few distributions. In this study, in the case that F is totally unknown, life table management and Kaplan-Meier estimator were used depending on random right-censored sampling for the estimation of F value. Then, for the estimation of the renewal function value in the random right-censored data, nonparametric estimators were proposed and the problem of how to calculate these estimators were discussed.

Keywords

References

  1. [1] Ross, M.S., 1983. Stochastic Processes. John Wiley&Sons, 510, New York.
  2. [2] Karlın, S., and Taylor H. M., 1975. A First Course in Stochastic Processes, Second edition. Acedemic Press, 557, New York, 1975.
  3. [3] Tijms, 1994. Stochastic Models: An Algorithmic Approach, John Wiley&Sons, New York, 1994.
  4. [4] Tamam D., 2008. Tam ve Sansürlü Örneklem Durumlarında Weibull Dağılımı için Bazı İstatistiki Sonuç Çıkarımları, Yüksek Lisans Tezi. Ankara Üniversitesi, Ankara.
  5. [5] Lawless, J. F., 2003. Statistical Models and Methods for Lifetime Data, John Wiley&Sons, 630, Canada.
  6. [6] Schneider H., Lın B. S., and O’cınneide C., 1990. Comparison of Nonparemetrik Estimators for the Renewal Function. Journal Roy. Statist. Soc. Ser. C., 39, 55-61.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Publication Date

December 27, 2019

Submission Date

April 20, 2019

Acceptance Date

December 9, 2019

Published in Issue

Year 2019 Volume: 9 Number: 2

APA
Cengiz, Ç. (2019). Nonparametric estimation of a renewal function in the case of censored sample. Bitlis Eren University Journal of Science and Technology, 9(2), 54-57. https://doi.org/10.17678/beuscitech.556451
AMA
1.Cengiz Ç. Nonparametric estimation of a renewal function in the case of censored sample. Bitlis Eren University Journal of Science and Technology. 2019;9(2):54-57. doi:10.17678/beuscitech.556451
Chicago
Cengiz, Çiğdem. 2019. “Nonparametric Estimation of a Renewal Function in the Case of Censored Sample”. Bitlis Eren University Journal of Science and Technology 9 (2): 54-57. https://doi.org/10.17678/beuscitech.556451.
EndNote
Cengiz Ç (December 1, 2019) Nonparametric estimation of a renewal function in the case of censored sample. Bitlis Eren University Journal of Science and Technology 9 2 54–57.
IEEE
[1]Ç. Cengiz, “Nonparametric estimation of a renewal function in the case of censored sample”, Bitlis Eren University Journal of Science and Technology, vol. 9, no. 2, pp. 54–57, Dec. 2019, doi: 10.17678/beuscitech.556451.
ISNAD
Cengiz, Çiğdem. “Nonparametric Estimation of a Renewal Function in the Case of Censored Sample”. Bitlis Eren University Journal of Science and Technology 9/2 (December 1, 2019): 54-57. https://doi.org/10.17678/beuscitech.556451.
JAMA
1.Cengiz Ç. Nonparametric estimation of a renewal function in the case of censored sample. Bitlis Eren University Journal of Science and Technology. 2019;9:54–57.
MLA
Cengiz, Çiğdem. “Nonparametric Estimation of a Renewal Function in the Case of Censored Sample”. Bitlis Eren University Journal of Science and Technology, vol. 9, no. 2, Dec. 2019, pp. 54-57, doi:10.17678/beuscitech.556451.
Vancouver
1.Çiğdem Cengiz. Nonparametric estimation of a renewal function in the case of censored sample. Bitlis Eren University Journal of Science and Technology. 2019 Dec. 1;9(2):54-7. doi:10.17678/beuscitech.556451

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