Semiparametic Efficient Inference Methods in Complex Data Models

复杂数据模型中的半参数高效推理方法

• 批准号: RGPIN-2016-06002
• 负责人: Wang, Liqun
• 金额: $ 2.4万
• 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=635977
• 关键词: Semiparametic; Efficient; Inference; Methods; Complex

Nowadays more and more massive and high-dimensional data become available in many scientific fields such as medical and health science, astronomy and physics, computer science and engineering, as well as economics and finance. A challenging task in high-dimensional data analysis is how to select the most relevant predictors among a large number of candidate variables to accurately predict a response variable of interest. High-dimensional variable selection problem has drawn a lot of attention in statistics, as well as in computer science and engineering. However, most research has been focusing on linear models where all variables are assumed to be precisely measured. On the other hand, real data applications always involve nonlinear relationships and variables that are either not directly observable or imprecisely measured. Therefore it is of theoretical and practical interests to study the high-dimensional variable selection problem in measurement error models. One possible direction of investigation is novel regularization methods that incorporate instrumental variables. Moreover, nonlinear relationships will be investigated because they arise in many fields including compressive sensing, signal processing and imaging.

如今，医学卫生科学、天文物理、计算机科学与工程以及经济金融等诸多科学领域都有越来越多的海量高维数据可用。高维数据分析中的一个具有挑战性的任务是如何在大量的候选变量中选择最相关的预测值，以准确地预测感兴趣的响应变量。高维变量选择问题在统计学、计算机科学和工程等领域都受到了广泛的关注。然而，大多数研究一直集中在线性模型上，其中所有变量都被假设为精确测量。另一方面，实际数据应用总是涉及非线性关系和变量，这些关系和变量要么不能直接观察到，要么不能精确测量。因此，研究测量误差模型中的高维变量选择问题具有重要的理论意义和实用价值。一个可能的研究方向是结合工具变量的新的正则化方法。此外，还将研究非线性关系，因为它们出现在许多领域，包括压缩传感、信号处理和成像。