{"product_id":"9781119417385","title":"Statistical Learning for Big Dependent Data","description":"\u003ch3\u003eWiley Series in Probability and Statistics\u003c\/h3\u003e\u003ch1\u003eStatistical Learning for Big Dependent Data\u003c\/h1\u003e\u003ch3\u003eDaniel Peña | Ruey S. Tsay\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eMaster advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eStatistical Learning with Big Dependent Data\u003c\/i\u003e delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data.  Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests.  Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented.\u003c\/p\u003e \u003cp\u003eThroughout the book, the advantages and disadvantages of the methods discussed are given.  The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAnalysis of Big Dependent Data \u003c\/i\u003eincludes a wide variety of topics for modeling and understanding big dependent data, like:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNew ways to plot large sets of time series\u003c\/li\u003e \u003cli\u003eAn automatic procedure to build univariate ARMA models for individual components of a large data set\u003c\/li\u003e \u003cli\u003ePowerful outlier detection procedures for large sets of related time series\u003c\/li\u003e \u003cli\u003eNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series\u003c\/li\u003e \u003cli\u003eBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models\u003c\/li\u003e \u003cli\u003eDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series\u003c\/li\u003e \u003cli\u003eForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.\u003c\/li\u003e \u003cli\u003eIntroduction of modern procedures for modeling and forecasting spatio-temporal data \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for PhD students and researchers in business, economics, engineering, and science: \u003ci\u003eStatistical Learning with Big Dependent Data \u003c\/i\u003ealso belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eDaniel Peña, PhD,\u003c\/b\u003e is Professor of Statistics at Universidad Carlos III de Madrid, Spain. He received his PhD from Universidad Politecnica de Madrid in 1976 and has taught at the Universities of Wisconsin-Madison, Chicago and Carlos III de Madrid, where he was Rector from 2007 to 2015. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eRuey S. Tsay, PhD,\u003c\/b\u003e is the H.G.B Alexander Professor of Econometrics \u0026amp; Statistics at the Booth School of Business, University of Chicago, United States. He received his PhD in 1982 from the University of Wisconsin-Madison. His research focuses on areas of business and economic forecasting, financial econometrics, risk management, and analysis of big dependent data.\u003c\/p\u003e \u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e04 May 2021\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119417385\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e560\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e45.6\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44311316398220,"sku":"9781119417385","price":134.06,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119417385_ceaba691-5cf6-4128-9e58-ed91fba0a43b.jpg?v=1780114252","url":"https:\/\/lateknightbooks.com\/products\/9781119417385","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}