專書單篇
| 學年 | 105 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2017-02-03 |
| 作品名稱 | Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations |
| 作品名稱（其他語言） | |
| 著者 | HKT Ng; Y-J Lin; Tzong-Ru Tsai; YL Lio; N Jiang |
| 單位 | |
| 出版者 | Springer |
| 著錄名稱、卷期、頁數 | Monte-Carlo Simulation-Based Statistical Modeling |
| 摘要 | In the process of designing life-testing experiments , experimenters always establish the optimal experiment scheme based on a particular parametric lifetime model. In most applications, the true lifetime model is unknown and need to be specified for the determination of optimal experiment schemes. Misspecification of the lifetime model may lead to a substantial loss of efficiency in the statistical analysis. Moreover, the determination of the optimal experiment scheme is always relying on asymptotic statistical theory. Therefore, the optimal experiment scheme may not be optimal for finite sample cases. This chapter aims to provide a general framework to quantify the sensitivity and uncertainty of the optimal experiment scheme due to misspecification of the lifetime model. For the illustration of the methodology developed here, analytical and Monte-Carlo methods are employed to evaluate the robustness of the optimal experiment scheme for progressive Type-II censored experiment under the location-scale family of distributions. |
| 關鍵字 | Objective Function;Asymptotic Variance;Fisher Information Matrix;Model Misspecification;Lifetime Distribution |
| 語言 | en_US |
| ISBN | 9789811033070 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116737 ) |