Lecture
Probability and Statistics II: Estimation and Hypothesis Testing
Related lectures (34)
Inference and Mixed Models
Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.
Statistical Inference
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Confidence Intervals: Definition and Estimation
Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.
Statistical Theory: Inference and Optimality
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Likelihood Ratio Tests: Optimality and Extensions
Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.
Estimation Criteria
Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Basic Principles of Point Estimation
Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.
Estimating Parameters: Confidence Intervals
Explores estimating parameters through confidence intervals in linear regression and statistics.