Statistical Learning For Big Dependent Data
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Statistical Learning For Big Dependent Data
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Author : Daniel Peña
language : en
Publisher: John Wiley & Sons
Release Date : 2021-03-02
Statistical Learning For Big Dependent Data written by Daniel Peña and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Mathematics categories.
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.
Statistical Learning For Big Dependent Data
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Author : Daniel Peña
language : en
Publisher: John Wiley & Sons
Release Date : 2021-05-04
Statistical Learning For Big Dependent Data written by Daniel Peña and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-04 with Mathematics categories.
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.
Ccsp Certified Cloud Security Professional All In One Exam Guide Second Edition
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Author : Daniel Carter
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-12-06
Ccsp Certified Cloud Security Professional All In One Exam Guide Second Edition written by Daniel Carter and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-06 with Computers categories.
Comprehensive coverage of every domain on the CCSP exam This highly effective self-study guide covers all six domains of the 2019 release of the challenging Certified Cloud Security Professional exam as well as the CCSP Common Body of Knowledge, developed by the International Information Systems Security Certification Consortium (ISC)2®. CCSP Certified Cloud Security Professional All-in-One Exam Guide, Second Edition offers clear explanations, real-world examples, and practice questions that match the content, tone, and format of those on the actual exam. To aid in retention, each chapter includes exam tips that highlight key information, a summary that serves as a quick review of salient points, and practice questions that allow you to test your comprehension. “Notes,” “Tips,” and “Cautions” throughout provide additional insight. Covers all six exam domains: • Cloud Concepts, Architecture, and Design • Cloud Data Security • Cloud Platform & Infrastructure Security • Cloud Application Security • Cloud Security Operations • Legal, Risk, and Compliance Online content includes: • 300 practice questions
Memoirs Of The Institute Of Scientific And Industrial Research Osaka University
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Author : Ōsaka Daigaku. Sangyō Kagaku Kenkyūjo
language : en
Publisher:
Release Date : 2015
Memoirs Of The Institute Of Scientific And Industrial Research Osaka University written by Ōsaka Daigaku. Sangyō Kagaku Kenkyūjo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Science categories.
Ccsp Certified Cloud Security Professional All In One Exam Guide Third Edition
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Author : Daniel Carter
language : en
Publisher: McGraw Hill Professional
Release Date : 2022-11-25
Ccsp Certified Cloud Security Professional All In One Exam Guide Third Edition written by Daniel Carter and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.
This fully updated self-study guide delivers 100% coverage of all topics on the current version of the CCSP exam Thoroughly revised for the 2022 edition of the exam, this highly effective test preparation guide covers all six domains within the CCSP Body of Knowledge. The book offers clear explanations of every subject on the CCSP exam and features accurate practice questions and real-world examples. New, updated, or expanded coverage includes cloud data security, DevOps security, mobile computing, threat modeling paradigms, regulatory and legal frameworks, and best practices and standards. Written by a respected computer security expert, CCSP Certified Cloud Security Professional All-in-One Exam Guide, Third Edition is both a powerful study tool and a valuable reference that will serve professionals long after the test. To aid in self-study, each chapter includes exam tips that highlight key information, a summary that serves as a quick review of salient points, and practice questions that allow you to test your comprehension. Special design elements throughout provide insight and call out potentially harmful situations. All practice questions match the tone, content, and format of those on the actual exam Includes access to 300 practice questions in the TotalTesterTM Online customizable test engine Written by an IT security expert and experienced author
Proceedings In Computational Statistics
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Author : Albert Prat
language : en
Publisher:
Release Date : 1996
Proceedings In Computational Statistics written by Albert Prat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computer science categories.
COMPSTAT symposia have been held regularly since 1974 when they started in Vienna. This tradition has made COMPSTAT a major forum for the interplay of statistics and computer sciences with contributions from many well known scientists all over the world. The scientific programme of COMPSTAT '96 covers all aspects of this interplay, from user-experiences and evaluation of software through the development and implementation of new statistical ideas. All papers presented belong to one of the three following categories: - Statistical methods (preferable new ones) that require a substantial use of computing; - Computer environments, tools and software useful in statistics; - Applications of computational statistics in areas of substantial interest (environment, health, industry, biometrics, etc.).
Proceedings Of The Statistical Computing Section
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Author : American Statistical Association. Statistical Computing Section
language : en
Publisher:
Release Date : 1999
Proceedings Of The Statistical Computing Section written by American Statistical Association. Statistical Computing Section and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Mathematical statistics categories.
Papers presented at the annual meeting of the American Statistical Association ...
Compstat
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Author : Albert Prat
language : en
Publisher: Physica
Release Date : 1996
Compstat written by Albert Prat and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
COMPSTAT symposia have been held regularly since 1974 when they started in Vienna. This tradition has made COMPSTAT a major forum for the interplay of statistics and computer sciences with contributions from many well known scientists all over the world. The scientific programme of COMPSTAT '96 covers all aspects of this interplay, from user-experiences and evaluation of software through the development and implementation of new statistical ideas. All papers presented belong to one of the three following categories: - Statistical methods (preferable new ones) that require a substantial use of computing; - Computer environments, tools and software useful in statistics; - Applications of computational statistics in areas of substantial interest (environment, health, industry, biometrics, etc.).
Data Science Classification And Related Methods
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Author : International Federation of Classification Societies. Conference
language : en
Publisher: Springer
Release Date : 1998-03
Data Science Classification And Related Methods written by International Federation of Classification Societies. Conference and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-03 with Business & Economics categories.
This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery. The result is a broad view of the state of the art, making this an essential work not only for data analysts, mathematicians, and statisticians, but also for researchers involved in data processing at all stages from data gathering to decision making.
Living With Leviathan
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Author : Linda L. M. Bennett
language : en
Publisher:
Release Date : 1990
Living With Leviathan written by Linda L. M. Bennett and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Business & Economics categories.
Charting trends in American public opinion about big government from the 1930s to 1989, with emphasis on the last twenty-five years, they trace how we have adapted to a growing national government. They analyze what these opinions tell us about changing themes in American political culture and document the significant differences in public opinion about big government, the positive state, and citizen's obligations.