Lecture
Optimality in Statistical Inference
Related lectures (51)
Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals
Explores type I and type II errors, critical values, and confidence intervals in statistical significance.
Interval Estimation
Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.
Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Statistical Estimation
Covers statistical estimation methods and explores practical examples of parameter estimation for different distributions.
Statistical Theory: Inference and Sufficiency
Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.
Confidence Intervals and Hypothesis Tests
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
Statistical Estimation
Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Statistical Models: Basics & Applications
Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.
Conditional Probability Distributions
Covers conditional probability distributions and introduces the concept of conditional expected value.
Special Families of Models
Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.