{"product_id":"multivariate-density-estimation-theory-practice-and-visualization-hardcover","title":"Multivariate Density Estimation: Theory, Practice, and Visualization - 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\u003eDavid W. Scott\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eClarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFeaturing a thoroughly revised presentation, \u003ci\u003eMultivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.\u003c\/p\u003e \u003cp\u003eThe new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, \u003ci\u003eMultivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e also features: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eOver 150 updated figures to clarify theoretical results and to show analyses of real data sets\u003c\/li\u003e \u003cli\u003eAn updated presentation of graphic visualization using computer software such as R\u003c\/li\u003e \u003cli\u003eA clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering\u003c\/li\u003e \u003cli\u003eMore than 130 problems to help readers reinforce the main concepts and ideas presented\u003c\/li\u003e \u003cli\u003eBoxed theorems and results allowing easy identification of crucial ideas\u003c\/li\u003e \u003cli\u003eFigures in color in the digital versions of the book\u003c\/li\u003e \u003cli\u003eA website with related data sets\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMultivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eClarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003eFeaturing a thoroughly revised presentation, \u003ci\u003eMultivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the \u003ci\u003eSecond Edition\u003c\/i\u003e presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.\u003cbr\u003e \u003cbr\u003e The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the \u003ci\u003eSecond Edition\u003c\/i\u003e demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, \u003ci\u003eMultivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e features: \u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e \u003cul\u003e \u003cli\u003eOver 150 updated figures to clarify theoretical results and to show analyses of real data sets\u003c\/li\u003e \u003cli\u003eAn updated presentation of graphic visualization using computer software such as R\u003c\/li\u003e \u003cli\u003eA clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering\u003c\/li\u003e \u003cli\u003eOver 130 problems to help readers reinforce the main concepts and ideas presented\u003c\/li\u003e \u003cli\u003eBoxed theorems and results allowing easy identification of crucial ideas\u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003e\u003cbr\u003e Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition\u003c\/i\u003e is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as all readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The \u003ci\u003eSecond Edition\u003c\/i\u003e is also a useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions. \u003cbr\u003e \u003cbr\u003e \u003cb\u003e\u003ci\u003eDavid W. Scott, PhD, \u003c\/i\u003e\u003c\/b\u003e is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder's Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also Coeditor of \u003ci\u003eWiley Interdisciplinary Reviews: Computational Statistics\u003c\/i\u003e and previous Editor of the \u003ci\u003eJournal of Computational and Graphical Statistics\u003c\/i\u003e.\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eDavid W. Scott\u003c\/b\u003e, PhD, is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder's Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also coeditor of \u003ci\u003eWiley Interdisciplinary Reviews: Computational Statistics\u003c\/i\u003e and previous Editor of the \u003ci\u003eJournal of Computational and Graphical Statistics\u003c\/i\u003e.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 384\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1 x 9.3 x 6.3 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 30, 2015\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47337083994361,"sku":"9780471697558","price":178.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/rOZNlC8bk59780471697558.webp?v=1769674886","url":"https:\/\/bookscloud.io\/products\/multivariate-density-estimation-theory-practice-and-visualization-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}