Lectures related to Weighted arithmetic mean

Kernel Regression: Weighted Average and Feature Maps

Covers kernel regression and feature maps for data separability.

Assumption-lean Inference: Generalised Linear Model Parameters

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

Statistical Inference: Weighted Mean and Radioactive Decay

Introduces weighted mean calculation and radioactive decay concept with probability estimation.

Market Price Estimation

Explores acceptable and probable prices in real estate, estimation methods, and price comparison tricks.

Real Estate Market Trends

Explores the evolution of real estate prices, hedonic models, and the UBS Swiss Real Estate Bubble Index.

All of Probability: Basic Bounds, LLN & CLT

Introduces basic bounds, LLN, and CLT in probability theory, emphasizing convergence to normal distribution.

Monte Carlo Estimation: Error Analysis

Covers the Monte Carlo method for generating realizations and unbiased estimators.

ECDF and Quantiles

Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.

Financial Economics: Portfolio Choice

Explores financial economics, mean-variance portfolio choice, asset valuation, efficient frontiers, and risk-aversion impact.

Stochastic Simulation: Monte Carlo Method

Covers the properties and error estimates of the Monte Carlo method in stochastic simulation.

Weighted Average and Interest Rates

Explores the calculation of weighted averages and interest rates, emphasizing the importance of averages in financial analysis.

Mixture models: alternative specific variance

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Mean-Variance Efficient Frontier

Explores the Mean-Variance case, Market Equilibrium, CAPM, and Efficient Market Hypothesis.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Multivariate Gaussian Distribution

Covers the definition of multivariate Gaussian distribution and its properties, including moment generating function and linear combinations of variables.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Fitting data with one Gauss function

Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.

Central Limit Theorem: Illustration and Applications

Explores the Central Limit Theorem and its statistical implications in random variables.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.