摘要
Most of the recently developed methods on optimum planning for accelerated life tests (ALT) involve “guessing” values of parameters to be estimated, and substituting such guesses in the proposed solution to obtain the final testing plan. In reality, such guesses may be very different from true values of the parameters, leading to inefficient test plans. To address this problem, we propose a sequential Bayesian strategy for planning of ALTs and a Bayesian estimation procedure for updating the parameter estimates sequentially. The proposed approach is motivated by ALT for polymer composite materials, but are generally applicable to a wide range of testing scenarios. Through the proposed sequential Bayesian design, one can efficiently collect data and then make predictions for the field performance. We use extensive simulations to evaluate the properties of the proposed sequential test planning strategy. We compare the proposed method to various traditional non-sequential optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to existing non-sequential optimum designs. Supplementary materials for this article are available online.
| 原文 | English |
|---|---|
| 頁（從 - 到） | 472-483 |
| 頁數 | 12 |
| 期刊 | Technometrics |
| 卷 | 60 |
| 發行號 | 4 |
| DOIs | |
| 出版狀態 | Published - 2018 十月 2 |
指紋
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Applied Mathematics
引用此文
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Sequential Bayesian Design for Accelerated Life Tests. / Lee, I-Chen; Hong, Yili; Tseng, Sheng Tsaing; Dasgupta, Tirthankar.
於: Technometrics, 卷 60, 編號 4, 02.10.2018, p. 472-483.研究成果: Article
TY - JOUR
T1 - Sequential Bayesian Design for Accelerated Life Tests
AU - Lee, I-Chen
AU - Hong, Yili
AU - Tseng, Sheng Tsaing
AU - Dasgupta, Tirthankar
PY - 2018/10/2
Y1 - 2018/10/2
N2 - Most of the recently developed methods on optimum planning for accelerated life tests (ALT) involve “guessing” values of parameters to be estimated, and substituting such guesses in the proposed solution to obtain the final testing plan. In reality, such guesses may be very different from true values of the parameters, leading to inefficient test plans. To address this problem, we propose a sequential Bayesian strategy for planning of ALTs and a Bayesian estimation procedure for updating the parameter estimates sequentially. The proposed approach is motivated by ALT for polymer composite materials, but are generally applicable to a wide range of testing scenarios. Through the proposed sequential Bayesian design, one can efficiently collect data and then make predictions for the field performance. We use extensive simulations to evaluate the properties of the proposed sequential test planning strategy. We compare the proposed method to various traditional non-sequential optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to existing non-sequential optimum designs. Supplementary materials for this article are available online.
AB - Most of the recently developed methods on optimum planning for accelerated life tests (ALT) involve “guessing” values of parameters to be estimated, and substituting such guesses in the proposed solution to obtain the final testing plan. In reality, such guesses may be very different from true values of the parameters, leading to inefficient test plans. To address this problem, we propose a sequential Bayesian strategy for planning of ALTs and a Bayesian estimation procedure for updating the parameter estimates sequentially. The proposed approach is motivated by ALT for polymer composite materials, but are generally applicable to a wide range of testing scenarios. Through the proposed sequential Bayesian design, one can efficiently collect data and then make predictions for the field performance. We use extensive simulations to evaluate the properties of the proposed sequential test planning strategy. We compare the proposed method to various traditional non-sequential optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to existing non-sequential optimum designs. Supplementary materials for this article are available online.
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UR - http://www.scopus.com/inward/citedby.url?scp=85048080702&partnerID=8YFLogxK
U2 - 10.1080/00401706.2018.1437475
DO - 10.1080/00401706.2018.1437475
M3 - Article
AN - SCOPUS:85048080702
VL - 60
SP - 472
EP - 483
JO - Technometrics
JF - Technometrics
SN - 0040-1706
IS - 4
ER -