Parametric Embedding of Non-parametric Problems

非参数问题的参数嵌入

• 批准号: RGPIN-2017-03855
• 负责人: Alvo, Mayer
• 金额: $ 1.02万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636020
• 关键词: Parametric; Embedding; Non; parametric; Problems

Statistical inference with ranked data is inherently non-parametric. The strength of non-parametric statistics is that it can lead to efficient procedures while making few assumptions on the underlying distributions. On the other hand, non-parametric statistics does not generally rely on the use of a likelihood function, which limits the development of the usual inferential methods common in parametric statistics. Empirical likelihood methods have represented a major advance in this direction. Another advance consists of a parametric embedding of non-parametric inference problems to bridge the apparent gap between parametric and non-parametric statistics. My long term

排序数据的统计推断本质上是非参数的。非参数统计的优势在于，它可以导致有效的程序，同时对潜在的分布做出很少的假设。另一方面，非参数统计通常不依赖于似然函数的使用，这限制了参数统计中常见的通常推断方法的发展。经验似然方法代表了这一方向的重大进展。另一个进步包括非参数推断问题的参数嵌入，以弥合参数和非参数统计之间的明显差距。我的长期