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000938492 019__ $$a1164498724$$a1179000567$$a1179050085$$a1181842050$$a1181901963$$a1182455797
000938492 020__ $$a9783030401894$$q(electronic book)
000938492 020__ $$a3030401898$$q(electronic book)
000938492 020__ $$z9783030401887
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000938492 0247_ $$a10.1007/978-3-030-40
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000938492 050_4 $$aQA278.2$$b.B47 2020
000938492 08214 $$a519.5$$223
000938492 1001_ $$aBerk, Richard A.
000938492 24510 $$aStatistical learning from a regression perspective /$$cby Richard A. Berk.
000938492 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2020]
000938492 300__ $$a1 online resource
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000938492 337__ $$acomputer$$bc$$2rdamedia
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000938492 4901_ $$aSpringer Texts in Statistics,$$x1431-875X
000938492 504__ $$aIncludes bibliographical references and index.
000938492 5050_ $$aPreface -- Preface To Second Edition -- Preface To Third Edition -- 1 Statistical Learning as a Regression Problem -- 2 Splines, Smoothers, and Kernels -- 3 Classification and Regression Trees (CART) -- 4 Bagging -- 5 Random Forests -- 6 Boosting -- 7 Support Vector Machines -- 8 Neural Networks -- 9 Reinforcement Learning and Genetic Algorithms -- 10 Integration Themes and a Bit of Craft Lore -- Index.
000938492 506__ $$aAccess limited to authorized users.
000938492 520__ $$aThis 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.--$$cProvided by publisher.
000938492 650_0 $$aStatistics.
000938492 650_0 $$aProbabilities.
000938492 650_0 $$aPublic health.
000938492 650_0 $$aPsychology$$xMethodology.
000938492 650_0 $$aPsychometrics.
000938492 650_0 $$aSocial sciences.
000938492 77608 $$iPrint version:$$aBerk, Richard A.$$tStatistical Learning from a Regression Perspective$$dCham : Springer International Publishing AG,c2020$$z9783030401887
000938492 830_0 $$aSpringer texts in statistics.
000938492 852__ $$bebk
000938492 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-40189-4$$zOnline Access$$91397441.1
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