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Statistical Learning for Big Dependent Data by Daniel Peña at Abbey's Bookshop,
Statistical Learning for Big Dependent Data
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Daniel Peña Ruey S. Tsay (University of Chicago, IL, USA)

Statistical Learning for Big Dependent Data

Daniel Peña,  Ruey S. Tsay (University of Chicago, IL, USA)

$240.95

Hardback

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English
John Wiley & Sons Inc
29 March 2021
Probability & statistics
Series: Wiley Series in Probability and Statistics
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource

Statistical Learning with Big Dependent Data 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.

Throughout 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.

Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like:

New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data 

Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also 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.
By:   , , ,
Imprint:   John Wiley & Sons Inc
Country of Publication:   United States
Dimensions:   Height: 259mm,  Width: 185mm,  Spine: 31mm
Weight:   1.293kg
ISBN:   9781119417385
ISBN 10:   1119417384
Series:   Wiley Series in Probability and Statistics
Pages:   560
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Daniel Peña, PhD, 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. Ruey S. Tsay, PhD, is the H.G.B Alexander Professor of Econometrics & 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.

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