Time Series and Spectral Methods for Imputation, Regression, and Environmental Health

用于插补、回归和环境健康的时间序列和谱方法

• 批准号: RGPIN-2017-04741
• 负责人: Burr, Wesley
• 金额: $ 1.02万
• 依托单位: Trent 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=635818
• 关键词: Time; Series; Spectral; Methods; Imputation

Progress in statistical methods is vital for making sense of an ever-increasing flow of data. Time series are a type of data consisting of repeated observations of a physical phenomena, indexed by a common factor. The proposed research deals with the reconstruction of missing samples in such time series, the estimation of the spectra of them, and the use of both in regression models. The research falls within two distinct statistical areas, both dealing with data: the first in the pre-analysis stage (interpolating raw time series), and the second in the analysis stage (development of models for analysis).

统计方法的进步对于理解不断增长的数据流至关重要。时间序列是一种由物理现象的重复观测组成的数据类型，由公共因子索引。建议的研究涉及重建丢失的样本在这样的时间序列，估计的频谱，并使用两者的回归模型。研究福尔斯属于两个不同的统计领域，都处理数据：第一个在分析前阶段（内插原始时间序列），第二个在分析阶段（分析模型的开发）。