Druck auf Anfrage Neuware - Printed after ordering - 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.

Bestandsnummer des Verk�ufers 9783030429232

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Statistical Learning from a Regression Perspective
Verlag: Springer International Publishing, Springer Nature Switzerland
Erscheinungsjahr: 2021
Sprache: Englisch
ISBN-10: 3030429237
ISBN-13: 9783030429232
Einband: Taschenbuch
Auflage: 3. Auflage
Zustand: Neu

�ber diesen Titel

Inhaltsangabe

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.

�ber die Autorin bzw. den Autor

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.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

Das Unternehmen AHA-BUCH GmbH: Seit der Gr�ndung von AHA-BUCH im Juli 2005 ist unser Hauptziel, zufriedenen Kunden so schnell und so preisg�nstig wie m�glich ihren B�cherwunsch zu erf�llen. Unsere Firma besch�ftigt 16 Mitarbeiter, die nur ein Ziel kennen: den Kunden und seine W�nsche! Auf �ber 3700 m2 Fl�che haben wir �ber 100.000 B�cher, Modernes Antiquariat und Spiele auf Lager.

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

AHA-BUCH GmbH
Garlebsen 48, Einbeck, 37574, Germany

Gesch�ftsbedingungen und Versandinformationen

Verkaufsbedingungen

Allgemeine Gesch�ftsbedingungen und Kundeninformationen / Datenschutzerkl�rung

I. Allgemeine Gesch�ftsbedingungen

� 1 Grundlegende Bestimmungen

(1) Die nachstehenden Gesch�ftsbedingungen gelten f�r alle Vertr�ge, die Sie mit uns als Anbieter (AHA-BUCH GmbH) �ber die Internetplattformen AbeBooks und/oder ZVAB schlie�en. Soweit nicht anders vereinbart, wird der Einbeziehung gegebenenfalls von Ihnen verwendeter eigener Bedingungen widersprochen.

(2) Verbraucher im Sinne der nachstehenden Regelungen...

Mehr Information

Versandbedingungen

Wir liefern Lagerartikel innerhalb von 24 Stunden nach Erhalt der Bestellung aus.
Barsortimentsartikel, die wir �ber Nacht geliefert bekommen, am darauffolgenden Werktag.
Unser Ziel ist es Ihnen die Artikel in der �konomischten und effizientesten Weise zu senden.

Impressum & Info zum Verk�ufer