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Lecture
Maximum Likelihood Estimation: Multivariate Statistics
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Related lectures (30)
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Maximum Likelihood Theory & Applications
Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.
Hypothesis Testing and Confidence Intervals: An Overview
Covers hypothesis testing, confidence intervals, and their applications in statistics.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Multivariate Statistics: Introduction and Methods
Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.
Statistical Inference
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Likelihood Ratio Test: Hypothesis Testing
Explores hypothesis testing, emphasizing the likelihood ratio test and its applications in statistical analysis.
Likelihood Ratio Test: Neyman-Pearson Lemma
Explores likelihood ratio tests and the Neyman-Pearson Lemma for statistical hypothesis testing.
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.
Bayesian Statistics: Hypothesis Testing and Estimation
Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.
Copulas: Properties and Applications
Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.
