Springer Texts in Statistics

Statistical Learning from a Regression Perspective

Authors: Berk, Richard A.

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  • Provides accompanying, fully updated R code
  • Evaluates the ethical and political implications of the application of algorithmic methods
  • Features a new chapter on deep learning
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イーブック ¥9,151
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-030-40189-4
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ハードカバー ¥11,439
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-030-40188-7
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
この教本について

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture.

The third edition considers significant advances in recent years, among which are:

  • the development of overarching, conceptual frameworks for statistical learning;
  • the impact of  “big data” on statistical learning;
  • the nature and consequences of post-model selection statistical inference;
  • deep learning in various forms;
  • the special challenges to statistical inference posed by statistical learning;
  • the fundamental connections between data collection and data analysis;
  • interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy.

This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

著者について

Richard Berk is Distinguished Professor of Statistics Emeritus at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of statistical applications in the social and natural sciences.

Table of contents (10 chapters)

Table of contents (10 chapters)

書籍の購入

イーブック ¥9,151
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-030-40189-4
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF, EPUB
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ハードカバー ¥11,439
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-030-40188-7
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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書誌情報

Bibliographic Information
Book Title
Statistical Learning from a Regression Perspective
Authors
Series Title
Springer Texts in Statistics
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
イーブック ISBN
978-3-030-40189-4
DOI
10.1007/978-3-030-40189-4
ハードカバー ISBN
978-3-030-40188-7
Series ISSN
1431-875X
Edition Number
3
Number of Pages
XXVI, 433
Number of Illustrations
36 b/w illustrations, 107 illustrations in colour
Topics

*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.