Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Mixture models: alternative specific variance
Graph Chatbot
Related lectures (52)
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Estimation Methods in Probability and Statistics
Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.
Binary Choice Model
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Sampling: conditional maximum likelihood estimation
MOOC: Selected Topics on Discrete Choice
Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.
Mixtures: introduction
MOOC: Selected Topics on Discrete Choice
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Common Distributions: Moment Generating Functions
Explores common probability distributions, special distributions, and entropy concepts.
Model Specification: The Error Term
MOOC: Introduction to Discrete Choice Models
Delves into the binary choice model, error term specification, and Extreme Value distribution properties.
The Nested Logit Model
MOOC: Selected Topics on Discrete Choice
Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Horseshoe Crabs: Logistic Regression Analysis
Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.
Logistic Regression: Model Interpretation and Comparison
Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.
Mixture models: taste heterogeneity
MOOC: Selected Topics on Discrete Choice
Explores mixture models in discrete choice and random parameters estimation results.
Generalised Linear Models: Regression with Exponential Family Responses
Covers regression with exponential family responses using Generalised Linear Models.
Logistic Regression: Statistical Inference and Machine Learning
Covers logistic regression, likelihood function, Newton's method, and classification error estimation.
Maximum Likelihood Theory & Applications
Log in to Mediaspace to watch this video
Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.
Maximum Likelihood Estimation: Theory
Log in to Mediaspace to watch this video
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
MLE Applications: Binary Choice Models
Log in to Mediaspace to watch this video
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
Log in to Mediaspace to watch this video
Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Estimators and Confidence Intervals
Log in to Mediaspace to watch this video
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Previous
Page 1 of 3
Next
