审查(临床试验)
优化设计
贝叶斯概率
计算机科学
序贯分析
先验与后验
贝叶斯实验设计
贝叶斯线性回归
数学优化
统计
数学
贝叶斯推理
机器学习
人工智能
认识论
哲学
作者
Dustin Taylor,Steven E. Rigdon,Rong Pan,Douglas C. Montgomery
摘要
Abstract The assumption of normality is usually tied to the design and analysis of an experimental study. However, when dealing with lifetime testing and censoring at fixed time intervals, we can no longer assume that the outcomes will be normally distributed. This generally requires the use of optimal design techniques to construct the test plan for specific distribution of interest. Optimal designs in this situation depend on the parameters of the distribution, which are generally unknown a priori. A Bayesian approach can be used by placing a prior distribution on the parameters, thereby leading to an appropriate selection of experimental design. This, along with the model and number of predictors, can be used to derive the D‐optimal design for an allowed number of experimental runs. This paper explores using this Bayesian approach on various lifetime regression models to select appropriate D‐optimal designs in regular and irregular design regions.
科研通智能强力驱动
Strongly Powered by AbleSci AI