Dynamic Signal Detection in Non- and Semi-Parametric Models

非参数和半参数模型中的动态信号检测

• 批准号: 1812258
• 负责人: Lan Xue
• 金额: $ 10万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812258&HistoricalAwards=false
• 关键词: Dynamic; Signal; Detection; Non; Semi

In the big data era, massive data with complex structures are generated in an explosive fashion. Non- and semi-parametric models are powerful statistical tools for exploring nonlinear patterns hidden in complex data, and have been used in a wide range of fields, such as, biomedical science, geology, engineering and social sciences. However, traditional non- and semi-parametric methods are limited in their ability to deal with massive data of high dimensions. The goal of the proposed research is to develop effective tools for dynamic estimation and variable selection for non- and semi-parametric models in the massive complex data setting. The proposed research generates new methods and theory, and will provide practitioners in different fields with tools to better understand complex and dynamic structures in massive data. As an effective dimension reduction tool, variable selection is very useful for modeling high-dimensional data. In recent years, the properties of the penalized methods have been well investigated for both linear models and semiparametric models. However, those methods generally do not allow the variable selection to dynamically change with other variables. For many longitudinal or spatial data in practice, there is a need to develop a general framework for dynamic variable selection that allows possibly different sets of relevant variables to be selected in different time periods or at different spatial locations. The objectives of the proposed research include: (1) to develop a novel procedure for dynamic variable selection in the varying coefficient model; (2) to provide large sample properties to ensure that the proposed method provides an optimal solution when the sample size is sufficiently large; (3) to develop an efficient algorithm which allows one to obtain a higher percentage of correct-fitting even when the dimension of covariates is large; (4) to apply the dynamic variable selection to study time-varying network data ; (5) to develop an innovative penalized spline procedure with triangulations which plays the roles of dynamic local signal detection, as well as efficient estimation of sparse non-parametric functions on irregular domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在大数据时代，具有复杂结构的海量数据以爆发式的方式产生。非参数和半参数模型是探索复杂数据中隐藏的非线性模式的强大统计工具，已广泛应用于生物医学、地质、工程和社会科学等领域。然而，传统的非参数和半参数方法处理大量高维数据的能力有限。提出的研究目标是开发有效的工具来动态估计和变量选择的非参数和半参数模型在大量复杂的数据集。提出的研究产生了新的方法和理论，并将为不同领域的从业者提供更好地理解海量数据中复杂和动态结构的工具。变量选择作为一种有效的降维工具，对高维数据建模非常有用。近年来，惩罚方法的性质在线性模型和半参数模型上都得到了很好的研究。但是，这些方法通常不允许变量选择随其他变量动态变化。对于实践中的许多纵向或空间数据，需要制定一个动态变量选择的一般框架，允许在不同的时间段或不同的空间位置选择可能不同的相关变量集。本文的研究目标包括：(1)建立变系数模型中动态变量选择的新方法；(2)提供大样本特性，以确保所提出的方法在样本量足够大时提供最优解；(3)开发一种有效的算法，即使协变量的维数很大，也能获得较高的正确拟合百分比；(4)应用动态变量选择方法研究时变网络数据；(5)提出了一种新的惩罚样条方法，该方法具有动态局部信号检测和不规则域上稀疏非参数函数的有效估计等功能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Time‐varying feature selection for longitudinal analysis

用于纵向分析的时变特征选择

• DOI: 10.1002/sim.8412
• 发表时间: 2019
• 期刊: Statistics in Medicine
• 影响因子: 2
• 作者: Xue, Lan;Shu, Xinxin;Shi, Peibei;Wu, Colin O.;Qu, Annie
• 通讯作者: Qu, Annie

Information criterion for nonparametric model-assisted survey estimators

非参数模型辅助调查估计器的信息标准

• 发表时间: 2019
• 期刊: Journal of survey statistics and methodology
• 影响因子: 2.1
• 作者: James, A;Xue, L.;Lesser, V
• 通讯作者: Lesser, V

Time-varying correlation structure estimation and local feature detection for spatio-temporal data

时空数据的时变相关结构估计和局部特征检测

• 发表时间: 2018
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Xueying Zheng;Lan Xue;Annie Qu
• 通讯作者: Annie Qu

Canopy spray deposition and related mortality impacts of commonly used insecticides on Drosophila suzukii Matsumura (Diptera: Drosophilidae) populations in blueberry

• DOI: 10.1002/ps.5672
• 发表时间: 2019-12-05
• 期刊: PEST MANAGEMENT SCIENCE
• 影响因子: 4.1
• 作者: Mermer, Serhan;Pfab, Ferdinand;Walton, Vaughn M.
• 通讯作者: Walton, Vaughn M.

TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA

应用网络数据的时变估计和动态模型选择

• DOI: 10.5705/ss.202017.0218
• 发表时间: 2020-01-01
• 期刊: STATISTICA SINICA
• 影响因子: 1.4
• 作者: Xue，Lan;Shu，Xinxin;Qu，Annie
• 通讯作者: Qu，Annie