Monotone empirical bayes, non/semiparametric methods for nonlinear/hazards/censored regression and functional estimation

单调经验贝叶斯、非线性/危险/审查回归和函数估计的非/半参数方法

• 批准号: 4631-2007
• 负责人: Singh, Radhey
• 金额: $ 1.17万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=473867
• 关键词: Monotone; empirical; bayes; non; semiparametric

In many fields, like medicine, agriculture, industry, engineering, biological sciences, sociology, often situations involving sequences of similar but independent investigations arise. In such situations the parameter of interest often varies unpredictably as the sequence progresses with unknown probability distribution, and hence a minimum risk decision, what is usually called Baysian decision, can not be made. However, the information collected from the previous investigations can sometimes be utilized to formulate a decision, what is popularly known as empirical Bayes decision, with risk close to the minimum Bayes risk. Part of the objective of this research is to propose improved/monotone EB estimation/test procedures when the responses are modelled by some parametric distribution and to investigate the speed and the best possible speed with which the risk of these procedures approach to the minimum Bayes risk.

在许多领域，例如医学，农业，工程，工程，生物科学，社会学，通常涉及相似但独立研究的序列。在这种情况下，感兴趣的参数通常会随着序列的进展而随着未知概率分布的进展而变得不可预测，因此无法做出最小的风险决策，通常所谓的贝西决策。但是，有时可以利用从先前调查中收集的信息来制定决定，即普遍称为经验贝叶斯的决定，风险接近最低贝叶斯风险。这项研究的一部分是在响应以某些参数分布对响应进行建模并研究这些程序的风险将最小贝叶斯风险的速度和最佳速度进行模型时提出改进/单调EB估计/测试程序。