Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences.
This text provides a mathematical foundation for prediction theory and time series analysis using the geometry of Hilbert spaces. Emphasis is on foundation and structure, supported by theory, application and exercises to provide reinforcement and to extend discussions.
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