High-dimensional statistical inference in parametric and nonparametric models

参数和非参数模型中的高维统计推断

• 批准号: RGPIN-2016-06262
• 负责人: Stepanova, Natalia
• 金额: $ 1.6万
• 依托单位: Carleton University
• 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=635796
• 关键词: dimensional; statistical; inference; parametric; nonparametric

Recent advances in technology, engineering, and computing power, as well as problems in diverse areas such as genomics, clinical trials, cosmology, and climate studies have given rise to new types of inference problems that involve a very large number of unknown parameters and are known as high-dimensional inference or sparse inference problems. In general, it is difficult to treat these problems fully nonparametrically and provide procedures with nearly exact theoretical properties. The aim of the research proposal is to develop new and improve some existing inferential procedures of high-dimensional statistics. In doing so, the emphasis is on providing optimal and adaptive (not requiring the knowledge of unknown parameters of the statistical models) procedures in such areas of mathematical statistics as estimation theory, hypothesis testing theory, variable selection and classification. The main approach to be taken is nonparametric. This approach, adopted by mathematical statisticians on a worldwide scale, assumes that the parameter(s) entering the statistical models under study are infinite-dimensional. For instance, in nonparametric regression analysis, an unknown regression function mixed with weak noise is assumed to be a member of a large (infinite-dimensional) class of functions, rather than a known function depending on a finite number of unknown parameters, as in parametric regression analysis. The main criteria of goodness of a statistical procedure employed in this study is asymptotic minimaxity. This strong notion of optimality is commonly used in modern nonparametric statistical inference. We anticipate that the methods developed during the completion of this research proposal will find their usage in diverse fields such as clinical trials, astrophysics, economics, and information technology.

技术、工程和计算能力的最新进展，以及基因组学、临床试验、宇宙学和气候研究等不同领域的问题，已经产生了涉及大量未知参数的新型推理问题，称为高维推理或稀疏推理问题。在一般情况下，这是很难处理这些问题完全nonparametrically和提供的程序几乎精确的理论性质。本研究的目的是发展新的和改进一些现有的高维统计的推理过程。在这样做的时候，重点是提供最佳的和自适应的（不需要的统计模型的未知参数的知识）的程序，在这些领域的数理统计估计理论，假设检验理论，变量选择和分类。要采取的主要方法是非参数的。这种方法被数学统计学家在世界范围内采用，假设进入所研究的统计模型的参数是无限维的。例如，在非参数回归分析中，混合有弱噪声的未知回归函数被假设为大（无限维）函数类的成员，而不是像参数回归分析那样依赖于有限数量的未知参数的已知函数。在这项研究中采用的统计程序的主要标准是渐近极小极大。这种强最优性的概念通常用于现代非参数统计推断。我们预计，在完成这项研究计划的过程中开发的方法将在临床试验、天体物理学、经济学和信息技术等不同领域得到应用。