Model Selection Methods in Biostatistics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Generalized Linear Models

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

Model Selection: Non-Nested Model Selection

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

Regression Methods: Model Building and Inference

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

Risk Estimation: Mallows' CL and Cp

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

Penalization in Ridge Regression

Covers penalization in ridge regression, emphasizing the trade-off between bias and variance in regression models.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.