Lectures related to Statistical Estimation: Properties and Distributions

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

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Covers the fundamental concepts of statistics and their application in data science.

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Covers the estimation of distributions using samples and probability models.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

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

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

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

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

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.