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Lecture
Maximum Likelihood Theory & Applications
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Related lectures (31)
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Explores the IRLS algorithm for weighted least squares estimation in GLM.
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Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Generalised Linear Models: Regression with Exponential Family Responses
Covers regression with exponential family responses using Generalised Linear Models.
Mixture Models: Simulation-based Estimation
Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Likelihood Ratio Test: Hypothesis Testing
Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
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Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.
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Covers the fundamental concepts of statistics and their application in data science.
Logistic Regression: Modeling Binary Response Variables
Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.
Discrete Choice Analysis
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Estimation Methods in Probability and Statistics
Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.
Maximum Likelihood Estimation: Theory
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Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
MLE Applications: Binary Choice Models
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Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Generalized Linear Models: A Brief Review
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Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Probability Distributions in Environmental Studies
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Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.
Linear Regression Basics
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Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
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