Statistical Estimation: Properties and Distributions

Related lectures (29)

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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.