Maximum Likelihood Estimation: Part 2

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Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Horseshoe Crabs: Logistic Regression Analysis

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Radiation Sources

Explores radiation sources, including fast electron sources, heavy charged particle sources, and neutron sources, covering processes like beta decay, internal conversion, and Auger electrons.

Estimation: Measures of Performance

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Maximum Likelihood Estimation: Theory and Examples

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Radiation Sources: Overview

Explores radiation sources, including fast electrons, heavy charged particles, and neutrons, discussing their applications and characteristics.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.