Linear Model with Restrictions/Variable Selections–Supervised Machine Learning Including Random Forest Regression

Richard J. Butler, Matthew J. Butler and Barbara L. Wilson

Chapter 19 in Advanced Statistics for Health Research, 2023, pp 311-331 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The following sections are included:BackgroundLASSO (least absolute shrinkage and selection operator) Restrictions as an ExamplePredicting Nurse Job Satisfaction: Machine Learning AnalysesSupervised Machine Learning I: Linear Probability and Logistic Regression MethodsSupervised Machine Learning II: Random Forest Regressions and Out-of-Sample ROCsReferences

Keywords: Nursing; Physician; Healthcare; Public Health; Regression; Orthogonal Projection; Geometric View of Causal Inference; 2SLS; Two-Stage Least Squares; Instrumental Variables; Probits; Logits; Proportional Hazards; Cox Regressions; Quantile Regression; Random Forest Regression; Randomization; Matching; Propensity-Score; Differences-in-Differences; Regression Discontinuity; Fixed Effects; R; SAS; STATA; Research Examples; Empirical Rule; Applied Statistics; Confidence Intervals; VIF; Standard Beta; Histograms; Scatterplots; Regression Gini Index; COVID-19; Gender Wage Differentials; LASSO; Area Under The Curve; AUC; ROC; Decision Trees; Maximum Likelihood; GMM; Data Generating Process; Split Sample Instrumental Variables; Local Average Treatment Effects (LATE); Data Visualization; Omitted Variable Bias; Simultaneous Equations; Measurement Error; Supervised Machine Learning; Panel Data; RGI; Health Professionals (search for similar items in EconPapers)
JEL-codes: C1 I11 I15 (search for similar items in EconPapers)
Date: 2023
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