推论
随机性
协方差
计算机科学
统计推断
时态数据库
功能数据分析
数据挖掘
计量经济学
统计
人工智能
机器学习
数学
作者
Gregory P. Bopp,John F. Ensley,Piotr Kokoszka,Matthew Reimherr
出处
期刊:Wiley series in probability and statistics
日期:2021-12-10
卷期号:: 351-374
标识
DOI:10.1002/9781119387916.ch14
摘要
The objective of this chapter is to review some recent developments in statistical inference for spatio-temporal functional data. After introducing the basic structure of the data, we highlight some recent inferential procedures, including tests for randomness, a change-point in the mean, separability of the covariance, temporal trend tests, and inference about spatio–temporal extremes. These tools are illustrated on data gathered across Russian weather stations dating back to the late nineteenth century.
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