Optimal Treatment Regimes: Instrumental Variable Approaches

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Linear Regression: Model Adjustment and Parameter Estimation

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Instrumental Variables: Intuition and Estimation

Covers instrumental variables, addressing endogeneity issues in regression analysis through estimation techniques and practical examples.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

The Stein Phenomenon and Superefficiency

Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.

Assumption-lean Inference: Generalised Linear Model Parameters

Explores assumption-lean inference for statistical estimands in generalised linear models, emphasizing robust and generic approaches.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.