Estimation: Measures of Performance

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Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Exponential Family: Properties and Estimation

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Maximum Likelihood: Estimation and Inference

Introduces maximum likelihood estimation, discussing its properties and applications in statistical analysis.

Maximum Likelihood Estimation

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

Maximum Likelihood Estimation

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

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.

Statistical Models and Parameter Estimation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.