Coverage probability

Related lectures (32)

Page 3 of 4

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.

Explores constructing confidence intervals and MLE limit theorems for large samples.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Covers variance reduction techniques in stochastic simulation to improve output quantity estimation accuracy.

Explores low-discrepancy point sets in stochastic simulation and their construction algorithms.