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Statistical Learning From A Regression Perspective 2008 Edition at Meripustak

Statistical Learning From A Regression Perspective 2008 Edition

Books from same Author: Richard A. Berk

Books from same Publisher: Springer

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    • General Information  
    Author(s)Richard A. Berk
    PublisherSpringer
    ISBN9780387775005
    Pages360
    BindingHardback
    LanguageEnglish
    Publish YearAugust 2008

    Description

    Springer Statistical Learning From A Regression Perspective 2008 Edition by Richard A. Berk

    Statistical Learning from a Regression Perspective 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. As a first approximation this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging random forests boosting and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one's data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. Table of contents : Statistical Learning as a Regression Problem.- Regression Splines and Regression Smoothers.- Classification and Regression Trees (CART).- Bagging.- Random Forests.- Boosting.- Support Vector Machines.- Broader Implications and a Bit of Craft Lore.