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

统计方法的进步对于理解不断增加的数据流至关重要。时间序列是一种由对某一物理现象的重复观测组成的数据，由一个共同因素编成索引。本文主要研究了时间序列中缺失样本的重建、谱的估计以及两者在回归模型中的应用。研究分为两个不同的统计领域，都处理数据：第一个在预分析阶段（插入原始时间序列），第二个在分析阶段（开发分析模型）。

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).