Contents：
主講人：艾雪芳 博士 (中央研究院統計科學研究所)
摘要Abstract:
Degradation analysis has become the most important technique and efficient method for developing statistical models of highly reliable products. When there are measurement errors in monotonic degradation paths, the assumption of the non-monotonic model can lead to contradictions between physical/chemical mechanisms and statistical/engineering explanations. To settle the contradiction, this study presents an independent increment degradation-based process that simultaneously considers the unit-to-unit variability, within-unit variability, and measurement error in the degradation data. To estimate the model parameters, we use a quasi-Monte Carlo approach to overcome high-dimensional integrals of the likelihood function, in addition to providing a model-checking procedure to assess the validity of model assumptions. Some case studies are performed to demonstrate the flexibility and applicability of the proposed models.
備 註：本活動與SDGs連結如下：SDG4優質教育
Category：Faculty training in teaching
Time：
2021/11/23 16:00 ~ 2021/11/23 17:30
Registration period：
2021/10/29 16:30 ~ 2021/11/22 17:00
Location：
科學館 S433室
Duration：1.5 hours
Registration Limit：30 (Current Registrants：15）
Attendees：teachers、students、staff