인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Purpose: This paper reports on the corresponding guidelines to help users select a suitable estimation method. The guidelines were derived by evaluating the reliability estimation performance of different estimation methods.
Methods: Weibull distribution parameters were calculated using the least squares and maximum likelihood estimators, aling with the Bayesian methods. The scale and shape parameters were estimated to calculate the life-time. Finally, the analysis of variance was performed to compare the accuracy of the various methods.
Results: Bayesian methods, which employed prior information, exhibited a relatively high performance for all sample sizes. As the sample size increased, the performance was similar to that of the least squares and maximum likelihood estimators. The performance of the Bayesian methods fluctuated according to the prior information.
Conclusion: The reliability of various methods to analyze Weibull-distribution-based censoring data was analyzed. The results can be used in reliability assessment, to achieve the target reliability in the product development phase.
Methods: Weibull distribution parameters were calculated using the least squares and maximum likelihood estimators, aling with the Bayesian methods. The scale and shape parameters were estimated to calculate the life-time. Finally, the analysis of variance was performed to compare the accuracy of the various methods.
Results: Bayesian methods, which employed prior information, exhibited a relatively high performance for all sample sizes. As the sample size increased, the performance was similar to that of the least squares and maximum likelihood estimators. The performance of the Bayesian methods fluctuated according to the prior information.
Conclusion: The reliability of various methods to analyze Weibull-distribution-based censoring data was analyzed. The results can be used in reliability assessment, to achieve the target reliability in the product development phase.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2020-323-001171784
