Nonparametric Methods for Temporally Correlated and High Dimensional Data

用于时间相关和高维数据的非参数方法

• 批准号: RGPIN-2014-04311
• 负责人: Takahara, Glen
• 金额: $ 0.8万
• 依托单位: Queen's 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=635819
• 关键词: Nonparametric; Methods; Temporally; Correlated; Dimensional

Progress in modern statistical methods remains vital in the face of challenges put forth by ever increasing amounts of data, the need to obtain accurate information from data that is increasingly pivotal in important decision processes, and the opportunities afforded by increased computing power. Nonparametric statistical methods, which is to say methods that place few and very weak assumptions on the data, are attractive in virtually every field of application for their broad applicability.The proposed research falls in this area and centres on developing theory and applications in nonparametric Bayesian methods, spectral methods for time series, and the reduction of high dimensional data. These are three distinct areas within statistics of active current research, dealing with qualitatively different types of data.Inference in Bayesian nonparametric methods, like all Bayesian methods, is based on a passage from a prior distribution to a posterior distribution over the quantity of interest which in the nonparametric case is not finite-dimensional. Examples are an unknown distribution, density function, or survival function. The desirable features of such methods are freeness from assumptions that the unknown quantity follows some parametric form as well as the wide range of inference possible once the posterior distribution is obtained in a workable form. The challenge with such methods is the theory and computation necessary to give posterior representations that are feasible to work with so as to draw inference from these representations efficiently. Spectral methods in time series analyze the data in the frequency domain. Some of the challenges in modern spectral methods include the problem of frequency aliasing and the difficult problems associated with nonstationary time series, including estimation of time varying spectra and testing for different forms of nonstationarity. A more pragmatic challenge involves the introduction of such methods into fields where such methods are traditionally not used, and we focus on environmental health risk and environmental epidemiolgy. The challenge in processing of high-dimensional data in statistics is to identify low dimensional structure in the data (which generally will be nonlinear) that can then be used to give a low dimensional representation while retaining the salient features of the data. TheThe work proposed here will address all of these challenges and lead to high quality, original research that will further the theory and applications in nonparametric statistical methods. The work in nonparametric Bayesian methods will give insight into the structure of posterior representations that will give rise to new computational methods for inference and grouping of data structure. The work in spectral methods for time series will advance methods for dealing with aliasing effects and nonstationarity in time series, which has wide ranging potential downstream implications, not the least of which is a better understanding of the processes defining our physical world. A further downstream impact will be to refine and improve the interpretability and reliability of risk estimation in the fields of environmental health risk and environmental epidemiology, which can ultimately affect the policies we make that pertain to the health of Canadians. The work in reduction of high dimensional data is ultimately a step towards opening up powerful statistical methodologies designed for low dimensional data to the ever increasing complexities of modern data.Training is a major component of this research program, which will support 5 Phd and 5 MSc graduate students, 4 undergraduate summer research students, and 2 to 3 Postdoctoral Fellows in total.

面对数据量不断增加所带来的挑战，需要从在重要决策过程中日益关键的数据中获得准确信息，以及计算能力的提高所带来的机会，现代统计方法的进展仍然至关重要。非参数统计方法，也就是说方法，很少和非常弱的假设的数据，是有吸引力的，几乎在每一个领域的应用，其广泛的application.The拟议的研究福尔斯落在这一领域的发展理论和应用的非参数贝叶斯方法，时间序列的谱方法，并减少高维数据的中心。这是三个不同的领域内的统计活动目前的研究，处理定性不同类型的data.Inference在贝叶斯非参数方法，像所有的贝叶斯方法，是基于一个通道，从先验分布到后验分布的数量感兴趣的，在非参数的情况下，是不是有限维的。例如未知分布、密度函数或生存函数。这种方法的理想特征是不受假设的约束，即未知量遵循某种参数形式，以及一旦以可行的形式获得后验分布，就可以进行广泛的推断。这种方法的挑战是理论和计算必要的后验表示是可行的工作，以便有效地从这些表示中得出推论。时间序列中的谱方法在频域中分析数据。现代谱方法中的一些挑战包括频率混叠问题和与非平稳时间序列相关的困难问题，包括时变谱的估计和不同形式的非平稳性的测试。一个更务实的挑战是将这些方法引入传统上不使用这些方法的领域，我们专注于环境健康风险和环境流行病学。在统计学中处理高维数据的挑战是识别数据中的低维结构（通常是非线性的），然后可以用来给出低维表示，同时保留数据的显着特征。这里提出的工作将解决所有这些挑战，并导致高质量的，原创性的研究，将进一步在非参数统计方法的理论和应用。非参数贝叶斯方法的工作将深入了解后验表示的结构，这将产生新的计算方法，用于数据结构的推理和分组。时间序列谱方法的工作将推进处理时间序列中混叠效应和非平稳性的方法，这具有广泛的潜在下游影响，其中最重要的是更好地理解定义我们物理世界的过程。进一步的下游影响将是完善和提高环境健康风险和环境流行病学领域风险估计的可解释性和可靠性，这最终会影响我们制定的与加拿大人健康有关的政策。在减少高维数据的工作最终是一个开放的强大的统计方法，为低维数据设计的现代数据的日益复杂的一步。培训是这个研究计划的主要组成部分，这将支持5个博士和5个硕士研究生，4个本科生暑期研究生，和2至3个博士后研究员总数。