Variable Selection Methods

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Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Regression Methods: Model Building and Diagnostics

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Linear Regression: Basics and Applications

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.