{"product_id":"high-dimensional-covariance-estimation-hardcover","title":"High-Dimensional Covariance Estimation - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMohsen Pourahmadi\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMethods for estimating sparse and large covariance matrices\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCovariance 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. \u003ci\u003eHigh-Dimensional Covariance Estimation \u003c\/i\u003eprovides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning.\u003c\/p\u003e \u003cp\u003eRecently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. \u003ci\u003eHigh-Dimensional Covariance Estimation\u003c\/i\u003e focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHigh-Dimensional Covariance Estimation\u003c\/i\u003e features chapters on: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eData, Sparsity, and Regularization\u003c\/li\u003e \u003cli\u003eRegularizing the Eigenstructure\u003c\/li\u003e \u003cli\u003eBanding, Tapering, and Thresholding\u003c\/li\u003e \u003cli\u003eCovariance Matrices\u003c\/li\u003e \u003cli\u003eSparse Gaussian Graphical Models\u003c\/li\u003e \u003cli\u003eMultivariate Regression\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMethods for estimating sparse and large covariance matrices\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCovariance 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. \u003ci\u003eHigh-Dimensional Covariance Estimation\u003c\/i\u003e provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning.\u003c\/p\u003e \u003cp\u003eRecently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. \u003ci\u003eHigh-Dimensional Covariance Estimation\u003c\/i\u003e focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHigh-Dimensional Covariance Estimation\u003c\/i\u003e features chapters on: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eData, Sparsity, and Regularization\u003c\/li\u003e \u003cli\u003eRegularizing the Eigenstructure\u003c\/li\u003e \u003cli\u003eBanding, Tapering, and Thresholding\u003c\/li\u003e \u003cli\u003eCovariance Matrices\u003c\/li\u003e \u003cli\u003eSparse Gaussian Graphical Models\u003c\/li\u003e \u003cli\u003eMultivariate Regression\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMOHSEN POURAHMADI, PhD, \u003c\/b\u003e is Professor of Statistics at Texas A\u0026amp;M University. He is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a member of the American Mathematical Society. Dr. Pourahmadi is the author of \u003ci\u003eFoundations of Time Series Analysis and Prediction Theory, \u003c\/i\u003e also published by Wiley.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 208\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.8 x 9.3 x 6.1 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 24, 2013\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47214285881593,"sku":"9781118034293","price":148.25,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/1FIo8TVbQd9781118034293.webp?v=1768112532","url":"https:\/\/bookscloud.io\/products\/high-dimensional-covariance-estimation-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}