Covariance analysis and beyond /

This book demonstrates the application of covariance matrices through cutting-edge models and practical applications, as well as extensions induced by multivariate data and other related subjects. In data analysis, when studying the relationships among a set of variables, the covariance matrix plays...

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Bibliographic Details
Main Authors: Lan, Wei (Of Xi nan cai jing da xue) (Author), Tsai, Chih-Ling (Author)
Format: eBook
Language:English
Published: Cham : Springer, [2026]
Subjects:

MARC

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520 |a This book demonstrates the application of covariance matrices through cutting-edge models and practical applications, as well as extensions induced by multivariate data and other related subjects. In data analysis, when studying the relationships among a set of variables, the covariance matrix plays an important role. It has been commonly and widely used across many fields, including agriculture, biology, business, communications, economics, engineering, finance, marketing, mathematics, medicine, data science, and social science, regardless of whether the data is dense or sparse, low-dimension or high-dimension, time series or non-time series, structured or unstructured, fixed or random, and training (learning) data or testing data. The covariance matrix is fundamental for extracting valuable information from multivariate data, such that this classical tool can be influential in modern data science and innovative statistical models.--  |c Provided by publisher. 
505 0 |a Introduction -- Covariance matrices, precision (concentration) matrices, estimations, and tests -- Structured covariance matrices and unconstrained parameterization -- Covariance regression models -- Covariance-mean regression models -- Fixed and random covariance models -- Spatial autoregressive and network autocorrelation models -- Factor models and covariance matrices -- Machine learning and covariance matrices -- Tensor and covariance matrices. 
532 8 |3 PDF  |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. 
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650 0 |a Analysis of covariance. 
650 6 |a Analyse de covariance. 
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