Topics in Statistical Modelling and Inference with High-Dimensional, Complex Data

高维、复杂数据的统计建模和推理主题

• 批准号: RGPIN-2017-05720
• 负责人: Wu, Yuehua
• 金额: $ 3.13万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668998
• 关键词: Topics; Statistical; Modelling; Inference; Dimensional

Technology is changing our lives. Data is now collected in large volumes at various granularities. Such data often exhibit complex structures. Statistical models able to capture this complexity can further our understanding of the underlying data-generating mechanism and advance relevant fields in science and engineering. However, modelling such data can be challenging, particularly when we have endogenous measurements, outliers, missing observations, or other anomalies.******The objective of this proposed research program is to tackle statistical problems encountered in modelling high-dimensional, complex data, with particular interest on spatio-temporal, financial, business intelligence, genomic data. It will focus on development of robust, computationally feasible and fast model selection procedures for noisy high- or ultra high-dimensional, complex data in the scenario that the set of candidate models may not contain the true one. It will develop a two-stage regularization method based multiple change-point detection in general model formulations, e.g., generalized linear models and functional data models, which will simultaneously estimate all the change-points and perform variable selection. It will deal with multiple change-point detection in high-dimensional regression models, which include high-dimensional linear dynamic panel data models and spatio-temporal regression models. It will further the development in regression clustering using respective regularization methods and stochastic search. It will propose non-stationary spatio-temporal modelling which leverage of geographical neighbourhood information. It will tackle spatio-temporal inverse modelling problems by effectively grouping grid data using penalized approaches. It will develop the feasible association rule mining for the massive business intelligence, and genomic data. It will address statistical modelling of financial data, e.g., long term implied volatility and trading data. For our proposed methods, we will carry out both theoretical and methodological investigations; in addition, we will develop computational algorithms to ensure that our methods are computationally feasible and efficient, and present results from numerical studies which validate our findings. The advancements achieved under the proposed research will produce significant impact in statistical modelling, inference, computing, and their applications in practice.

科技正在改变我们的生活。现在以各种粒度大量收集数据。这类数据往往表现出复杂的结构。能够捕捉这种复杂性的统计模型可以进一步理解潜在的数据生成机制，并推动科学和工程相关领域的发展。然而，对这些数据进行建模可能具有挑战性，特别是当我们有内源性测量值、异常值、缺失观测值或其他异常时。******这个研究计划的目标是解决在高维复杂数据建模中遇到的统计问题，特别对时空、金融、商业智能、基因组数据感兴趣。在候选模型集可能不包含真实模型的情况下，它将专注于开发具有鲁棒性、计算可行性和快速的模型选择程序，用于嘈杂的高维或超高维复杂数据。它将在一般模型公式中开发一种基于多变化点检测的两阶段正则化方法，例如广义线性模型和功能数据模型，它将同时估计所有的变化点并执行变量选择。它将处理高维回归模型中的多变化点检测，包括高维线性动态面板数据模型和时空回归模型。它将进一步发展回归聚类使用各自的正则化方法和随机搜索。提出利用地理邻域信息的非平稳时空模型。它将通过使用惩罚方法有效地分组网格数据来解决时空逆建模问题。为海量商业智能和基因组数据开发可行的关联规则挖掘方法。它将涉及金融数据的统计建模，例如，长期隐含波动率和交易数据。对于我们提出的方法，我们将进行理论和方法上的调查；此外，我们将开发计算算法，以确保我们的方法在计算上是可行的和有效的，并提出数值研究的结果，以验证我们的发现。所提出的研究成果将对统计建模、推理、计算及其在实践中的应用产生重大影响。