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Lecture
Testing Non Nested Hypotheses
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Related lectures (34)
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.
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: Likelihood Ratio Test
Covers the likelihood ratio test in choice models, benchmarking, and tests for taste variations and nonlinear specifications.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
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.
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.
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.
Classification Detection
Covers binary hypothesis testing and decision functions in specific scenarios.
Bayesian Statistics: Hypothesis Testing and Estimation
Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.
Hypothesis Testing: Neyman-Pearson Framework
On hypothesis testing explores the Neyman-Pearson framework, test functions, errors, and likelihood ratio tests.
Discrete Choice Analysis
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Latent Variable Models
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
Logistic Regression: Model Interpretation and Comparison
Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.
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.
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