Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations

doi:10.1007/978-981-10-3307-0_6

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題名:	Quantifying the Uncertainty in Optimal Experiment Schemes via Monte-Carlo Simulations
作者:	Ng, HKT;Lin, Y-J;Tsai, Tzong-Ru;Lio, YL;Jiang, N
關鍵詞:	Objective Function;Asymptotic Variance;Fisher Information Matrix;Model Misspecification;Lifetime Distribution
日期:	2017-02-03
上傳時間:	2019-05-18 12:12:32 (UTC+8)
出版者:	Springer
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.
關聯:	Monte-Carlo Simulation-Based Statistical Modeling
DOI:	10.1007/978-981-10-3307-0_6
顯示於類別:	[統計學系暨研究所] 專書之單篇

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