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Applied Regularization Methods for the Social Sciences

Holmes Finch
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Author_Holmes Finch
Bayesian inference applications
Bayesian Lasso
BIC Value
Category=JHBC
Category=PBT
Cluster Solution
Credibility Interval
Dichotomous Logistic Regression
Elastic Net
Elastic Net Estimator
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Estimate StdErr
feature selection
Grouped Lasso
high dimensional data analysis
Lasso Estimator
Lasso Penalty
latent variable models
machine learning
Min 1Q Median 3Q Max
Model Parameter Estimates
multilevel models
multivariate statistical methods
NA NA
NA NA NA
OLS Regression
Optimal Tuning Parameter
penalized regression models
Poisson Regression
Poisson Regression Model
Posterior Distribution
R SAS SPSS examples
Regression Model
regularization for social science research
Regularization Methods
Regularization Parameter
Ridge Estimator
statistical learning techniques
Tuning Parameter

Product details

  • ISBN 9780367408787
  • Weight: 625g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Mar 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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
  • Website containing all datasets and software scripts used in the examples
  • Inclusion of both frequentist and Bayesian regularization approaches
  • Application exercises for each chapter that instructors could use in class, and independent researchers could use to practice what they have learned from the book
About Holmes Finch

Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at BSU, and a professor of statistics and psychometrics. His research interests include structural equation modeling, item response theory, educational and psychological measurement, multilevel modeling, machine learning, and robust multivariate inference. In addition to conducting research in the field of statistics, he also regularly collaborates with colleagues in fields such as educational psychology, neuropsychology, and exercise physiology.

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