Estimation and Method of Moments

Related lectures (30)

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

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

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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

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

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

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

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

Page 2 of 2