Maximum Likelihood Estimation: Part 2

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Estimation Methods

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Nuclear Chemistry: Basics

Introduces the basics of nuclear chemistry, covering the nature of the nucleus and different types of radioactive decay.

Maximum Likelihood Estimation: Properties and Applications

Covers Maximum Likelihood Estimation properties, applications, and assumptions, providing a comprehensive understanding of MLE concepts and their practical implications.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Maximum Likelihood Estimation: Econometrics

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Exponential Functions

Explores the properties of exponential functions, including growth, decay, and their relationship with logarithmic functions.

Modern Regression: Maximum Likelihood Estimation

Explores maximum likelihood estimation, profile log likelihood, inference on coefficients, quasi-likelihood, model comparison, and REML method.

Stochastic Simulation: Computation and Estimation

Covers computation and estimation in stochastic simulation, focusing on generating iid replicas and optimal importance sampling.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.