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Statistical Learning from a Regression Perspective

Medium: Buch
ISBN: 978-3-319-82969-2
Verlag: Springer International Publishing
Erscheinungstermin: 16.06.2018
Lieferfrist: bis zu 10 Tage

This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. 
This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. 
The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided.


Produkteigenschaften


  • Artikelnummer: 9783319829692
  • Medium: Buch
  • ISBN: 978-3-319-82969-2
  • Verlag: Springer International Publishing
  • Erscheinungstermin: 16.06.2018
  • Sprache(n): Englisch
  • Auflage: Softcover Nachdruck of the original 2. Auflage 2016
  • Serie: Springer Texts in Statistics
  • Produktform: Kartoniert, Previously published in hardcover
  • Gewicht: 569 g
  • Seiten: 347
  • Format (B x H x T): 155 x 235 x 21 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Statistical Learning as a Regression Problem.- Splines, Smoothers, and Kernels.- Classification and Regression Trees (CART).- Bagging.- Random Forests.- Boosting.- Support Vector Machines.- Some Other Procedures Briefly.- Broader Implications and a Bit of Craft Lore.