Statistical Methods for Regular Monitoring Data
规则监测数据的统计方法AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确结合多元时间序列、时间域谱表示与规则监测数据，因此归入时间序列方向。
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规则监测数据的统计方法
- 研究问题
- 固定监测网络按规则时间间隔采集的气象与环境数据，如何联合建模时间和空间依赖，尤其处理球面上不完全对称的时空结构？
- 方法与思路
- 作者结合多元时间序列与空间统计，通过旋转对称模型和仅在时间域作谱表示两条路径构建模型，并研究似然近似与诊断。
- 材料陈述的贡献
- 摘要称时间谱建模可形成部分非参数的非对称模型；在爱尔兰十一站十八年逐日风数据中，对多项数据特征取得良好拟合。
- 阅读关注点
- 应核对时空非对称性的定义、协方差有效性和似然近似，检查缺失数据限制，以及谱域和时空域诊断支持哪些拟合结论。
- 阅读边界
- 仅依据所给 Crossref 摘要；具体模型、近似误差和拟合指标未说明，不能将该数据集的拟合表现推广至其他监测网络。
原文摘要
Summary Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for modelling such data and ways of approximating the associated likelihood functions. Models for processes on the sphere crossed with time are emphasized, especially models that are not fully symmetric in space–time. Two approaches to obtaining such models are described. The first is to consider a rotated version of fully symmetric models for which we have explicit expressions for the covariance function. The second is based on a representation of space–time covariance functions that is spectral in just the time domain and is shown to lead to natural partially nonparametric asymmetric models on the sphere crossed with time. Various models are applied to a data set of daily winds at 11 sites in Ireland over 18 years. Spectral and space–time domain diagnostic procedures are used to assess the quality of the fits. The spectral-in-time modelling approach is shown to yield a good fit to many properties of the data and can be applied in a routine fashion relative to finding elaborate parametric models that describe the space–time dependences of the data about as well.