Kronecker-structured covariance models for multiway data

IF 11 Q1 STATISTICS & PROBABILITY
Yu Wang, Zeyu Sun, Dogyoon Song, A. Hero
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引用次数: 3

Abstract

: Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their infer- ence. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.
多路数据的kronecker结构协方差模型
许多应用程序产生多维度极高的多向数据。这种多路数据建模在多通道信号和视频处理中非常重要,其中传感器产生多索引数据,例如在空间,频率和时间维度上。我们将解决多向数据协方差表示的挑战,并回顾过去二十年来多向协方差统计建模的一些进展,重点是张量值协方差模型及其推断。我们将通过一个空间天气应用来说明:预测太阳活动区随时间的演变。
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来源期刊
Statistics Surveys
Statistics Surveys STATISTICS & PROBABILITY-
CiteScore
11.70
自引率
0.00%
发文量
5
期刊介绍: Statistics Surveys publishes survey articles in theoretical, computational, and applied statistics. The style of articles may range from reviews of recent research to graduate textbook exposition. Articles may be broad or narrow in scope. The essential requirements are a well specified topic and target audience, together with clear exposition. Statistics Surveys is sponsored by the American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and by the Statistical Society of Canada.
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