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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, this book provides and overview of a variety of models alongside clear examples of hands-on application.
List of contents
1. Introduction. 2. Theoretical underpinnings of regularization methods. 3. Regularization methods for linear models. 4. Regularization methods for generalized linear models. 5. Regularization methods for multivariate linear models. 6. Regularization methods for cluster analysis and principal components analysis. 7. Regularization methods for latent variable models. 8. Regularization methods for multilevel models. 9. Advanced topics in feature selection.
About the author
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.
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, this book provides and overview of a variety of models alongside clear examples of hands-on application.
