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: 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=635999
• 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.

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。