Approximate Maximum Product Spacings Estimation for a Weibull Distribution under Progressive Censoring

• 발행기관: 한국자료분석학회
• 저자명: 조수빈(Subin Cho) 이경준(Kyeongjun Lee)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.21 No.5, 2203~2213쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2019.10.31

영문 초록

It is usually recognized that the life-times of test items may not be registered exactly. Maximum product spacings (MPS) method is most suitable method, especially to those cases where one of the parameter have an unknown shifted origin. In this paper, we consider the maximum product spacings estimators (MPSE) of the shape and scale parameters and reliability function when the samples are progressive censored (PC) samples. However, maximum product spacings estimators cannot be derived in a closed form. So, we consider the approximate maximum product spacings estimators of the shape and scale parameters and reliability function when the samples are progressive censored samples. The proposed estimators are compared by performing the Monte Carlo (MC) simulation is presented. For estimating the parameters and reliability, the choice approximate maximum product spacings estimators seem to be a reasonable choice. Also, real data set based on progressive censoring scheme have been also analyzed.

목차

1. Introduction

2. Maximum product spacings estimation

3. Approximate maximum product spacings estimations

4. Numerical experiment

5. Conclusion

References

키워드

approximate maximum product spacings estimator maximum product spacings estimator progressive censoring Taylor series expansion Weibull distribution