Applied Regularization Methods for the Social Sciences
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Applied Regularization Methods for the Social Sciences
Finch, Holmes
Taylor & Francis Ltd
05/2024
297
Mole
9781032209470
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Lasso Estimator;machine learning;Ridge Estimator;feature selection;Min 1Q Median 3Q Max;multilevel models;Elastic Net;latent variable models;Regression Model;Elastic Net Estimator;Optimal Tuning Parameter;Lasso Penalty;Tuning Parameter;BIC Value;Posterior Distribution;NA NA NA;Dichotomous Logistic Regression;Regularization Methods;Poisson Regression Model;Credibility Interval;Grouped Lasso;Bayesian Lasso;Model Parameter Estimates;NA NA;Regularization Parameter;Poisson Regression;OLS Regression;Estimate StdErr;Cluster Solution
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Lasso Estimator;machine learning;Ridge Estimator;feature selection;Min 1Q Median 3Q Max;multilevel models;Elastic Net;latent variable models;Regression Model;Elastic Net Estimator;Optimal Tuning Parameter;Lasso Penalty;Tuning Parameter;BIC Value;Posterior Distribution;NA NA NA;Dichotomous Logistic Regression;Regularization Methods;Poisson Regression Model;Credibility Interval;Grouped Lasso;Bayesian Lasso;Model Parameter Estimates;NA NA;Regularization Parameter;Poisson Regression;OLS Regression;Estimate StdErr;Cluster Solution
