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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2026]
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| Subjects: |
| Summary: | 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.-- |
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| Physical Description: | 1 online resource (xiv, 251 pages) : illustrations |
| Bibliography: | Includes bibliographical references and indexes. |
| ISBN: | 9783032087966 (electronic bk.) 3032087961 |