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Related lectures (32)

Likelihood Ratio Test: Hypothesis Testing

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

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Hypothesis Testing: Wilks' Theorem and P-Value

Explores hypothesis testing, Wilks' theorem, p-values, confidence intervals, and pivotal quantities.

Likelihood Ratio Test: Hypothesis Testing

Explores hypothesis testing, emphasizing the likelihood ratio test and its applications in statistical analysis.

Testing Non Nested Hypotheses

Focuses on testing non-nested hypotheses in choice models using the likelihood ratio test and the Cox test.

Hypothesis Testing in Statistics

Delves into hypothesis testing, covering test statistics, critical regions, power functions, p-values, multiple testing, and non-parametric statistics.

Hypothesis Testing: Statistics Overview

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Maximum Likelihood Estimation: Multivariate Statistics

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

Statistical Tests: Wald and p-values

Explores statistical tests like the Wald test and p-values, emphasizing their calculation and interpretation.

Statistical Tests for Exponential Families

Covers the optimal statistical tests for exponential families and the use of approximations in hypothesis testing.

Hypothesis Testing: Wilks' Theorem

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Detection & Estimation

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Non nested hypotheses MOOC: Introduction to Discrete Choice Models

Covers the comparison of restricted and unrestricted choice models using the likelihood ratio test and the Cox test.

Bayesian Statistics: Hypothesis Testing and Estimation

Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.

Logistic Regression: Model Interpretation and Comparison

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Likelihood Ratio Test MOOC: Introduction to Discrete Choice Models

Covers the Likelihood Ratio Test in choice models, comparing unrestricted and restricted models through benchmarking and testing different model specifications.

DOE Qualitative factors: IV

Explores qualitative factors in Design of Experiments, including statistical tests and error types.