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Lecture
Maximum Likelihood Estimation: Properties and Applications
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Related lectures (31)
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Statistical Justification of Least Squares
Explores the statistical justification of Least Squares and Generalized Linear Models.
Exponential Family
Covers the properties of the exponential family and the estimation of parameters.
Estimation: Measures of Performance
Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.
Likelihood Ratio Test: Hypothesis Testing
Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Special Families of Models
Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.
L-Moment Estimation: Probability-Weighted Moments
Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.
Statistical Hypothesis Testing
Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Statistical Theory: Maximum Likelihood Estimation
Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
