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，如医学、农业、工业、工程、生物科学、社会学，经常出现涉及类似但独立的调查序列的情况。在这种情况下，随着序列的发展，感兴趣的参数通常会随着未知的概率分布而发生不可预测的变化，因此无法做出最小风险决策，即通常所说的贝叶斯决策。然而，从以前的调查中收集的信息有时可以用来制定决策，即通常所说的经验贝叶斯决策，其风险接近最小贝叶斯风险。本研究的部分目的是在响应由某些参数分布建模时提出改进的/单调EB估计/测试程序，并调查这些程序的风险接近最小贝叶斯风险的速度和最佳可能速度。