Non nested hypotheses

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Related lectures (36)

Likelihood Ratio Test: Detection & Estimation

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

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.

Testing Non Nested Hypotheses

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

Maximum Likelihood Estimation: Multivariate Statistics

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

Testing: Likelihood Ratio Test

Covers the likelihood ratio test in choice models, benchmarking, and tests for taste variations and nonlinear specifications.

Likelihood Ratio Test: Hypothesis Testing

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

Statistical Tests for Exponential Families

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

Likelihood Ratio Test: Hypothesis Testing

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

Hypothesis Testing: Wilks' Theorem and P-Value

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

Statistical Inference

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Detection & Estimation

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

Hypothesis Testing: Wilks' Theorem

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

Statistical Tests: Wald and p-values

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

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Hypothesis Testing: Neyman-Pearson Framework

On hypothesis testing explores the Neyman-Pearson framework, test functions, errors, and likelihood ratio tests.

Bayesian Statistics: Hypothesis Testing and Estimation

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

Likelihood of a spike train MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

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