Nonparametric Function Estimation

非参数函数估计

• 批准号: RGPIN-2015-04058
• 负责人: Leblanc, Alexandre
• 金额: $ 0.8万
• 依托单位: University of Manitoba
• 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=636013
• 关键词: Nonparametric; Function; Estimation

Statistical Function Estimation is extremely important in many areas of scientific research. At the heart of many problems is the need to construct a curve that summarizes some important aspect of a set of data, be it a histogram, a survival curve or a response curve. Doing this well is of utmost importance for experimenters, but also for administrators, decision makers and government officials of all kinds. It is often also important that the methods used and provided solutions be simple to interpret and generally valid, without imposing theoretical restrictions and/or assumptions that are difficult or even impossible to verify in practice. Linked to this is also the need for objective methodologies that are not too dependent on a specific form of model, again something that can be hard to justify. This objectivity takes many forms, but naturally leads to studying the so-called

统计函数估计在科学研究的许多领域都非常重要。许多问题的核心是需要构建一条曲线来总结一组数据的某些重要方面，无论是直方图、生存曲线还是响应曲线。做好这一点对实验者来说至关重要，对管理者、决策者和各类政府官员来说也是如此。同样重要的是，所使用的方法和所提供的解决办法应易于解释和普遍有效，而不施加难以或甚至不可能在实践中验证的理论限制和/或假设。与此相关的还有对客观方法的需求，这种方法不太依赖于特定形式的模型，这也是很难证明的。这种客观性有许多形式，但自然导致了对所谓的研究