Lectures related to Estimation statistics

Hypothesis Testing: Wilks' Theorem

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Spatial Autocorrelation: Global Overview

Explores spatial neighborhood determination, weighting schemes, Moran's I index, and significance estimation.

Statistical Theory: Decision Theory Framework

Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.

Estimation Methods: Bias-Variance Tradeoff

Explores the MSE quality measure for estimators and the bias-variance tradeoff.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Understanding Statistics & Experimental Design

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Relational Query Optimization

Covers relational query optimization, including logical and physical query plans, cost estimation, equivalences, and the System R strategy.

Statistical Estimation: Properties and Distributions

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Uncertainty and Significant Figures

Explains confidence intervals, margin of error, pivots, and significant figures in statistical estimation.

Understanding Statistics & Experimental Design

Covers basic probability theory, ANOVA, experimental design, and statistical reporting errors.

Hypothesis Testing: Wilks' Theorem and P-Value

Explores hypothesis testing, Wilks' theorem, p-values, confidence intervals, and pivotal quantities.

Hypothesis Testing: A Different Perspective

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.

Probability and Estimation in Statistics

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

Estimating Moments: GEV and GPD

Explores moment estimation in GEV and GPD models, including L-moment estimation and robust parameter estimation.

Boolean Hidden Matching Problem

Covers the Boolean Hidden Matching Problem and its applications to lower bounds.