Description

Book Synopsis

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.

Key Features:

  • Description of regularization methods in a user friendly and easy to read manner
  • Inclusion of regularization-based approaches for a variety of statistical analyses commonly used in the social sciences, including both univariate and multivariate models
  • Fully developed extended examples using multiple software packages, including R, SAS, and SPSS

Applied Regularization Methods for the Social

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    RRP £44.99 – you save £2.25 (5%)

    Order before 4pm tomorrow for delivery by Thu 25 Jun 2026.

    A Paperback by Holmes Finch

    15 in stock


      View other formats and editions of Applied Regularization Methods for the Social by Holmes Finch

      Publisher: Taylor & Francis Ltd
      Publication Date: 1/27/2024
      ISBN13: 9781032209470, 978-1032209470
      ISBN10: 103220947X
      Also in:
      Psychological methodology

      Description

      Book Synopsis

      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.

      Key Features:

      • Description of regularization methods in a user friendly and easy to read manner
      • Inclusion of regularization-based approaches for a variety of statistical analyses commonly used in the social sciences, including both univariate and multivariate models
      • Fully developed extended examples using multiple software packages, including R, SAS, and SPSS

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