Applied regularization methods for the social sciences / Holmes Finch.

By:

Finch, W. Holmes (William Holmes) [author.]

Language: English Publication details: Boca Raton : CRC, 2022.Edition: First editionDescription: vii, 297pISBN:

9781032209470

Subject(s):

DDC classification:

300.15 FIN-A

Contents:

R -- Theoretical underpinnings of regularization methods -- Regularization methods for linear models -- Regularization methods for generalized linear models -- Regularization methods for multivariate linear models -- Regularization methods for cluster analysis and principal components analysis -- Regularization methods for latent variable models -- Regularization methods for multilevel models.

Summary: "Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action"--

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Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Books	NASSDOC Library				300.15 FIN-A (Browse shelf(Opens below))					Available			52812

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Previous	300.1223 ELL-I Introduction to contemporary social theory /	300.14 HOD-A Analyzing documentary accounts	300.14 WEB-B Basic content analysis	300.15 FIN-A Applied regularization methods for the social sciences /	300.151 BAR-; Stochastic models for social processes	300.151 BAR-; Stochastic models for social processes	300.151 BAR-; RBOOKB Simple models of group behavior	Next

Includes bibliographical references and index.

"Researchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action"--

English.

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