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Concept

Statistical parameter

Related lectures (32)

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

The cross-nested logit model MOOC: Selected Topics on Discrete Choice

Explores the cross-nested logit model, defining alternative groups with overlapping memberships and scale parameters for transportation analysis.

Heteroscedasticity: Modeling the Utility Function MOOC: Introduction to Discrete Choice Models

Explores heteroscedasticity in nonlinear specifications and the modeling of scale parameters.

Series entières: Définitions MOOC: Analysis I MOOC: Analysis I (part 6) : Study of functions, limited developments

Covers the definition of a power series and its convergence based on the choice of parameter x.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Generalized Linear Models: Examples and Applications

Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.

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Renormalization Group: Universal Critical Exponents

Explores the universal critical exponents of microscopic systems and the role of the renormalization group.

Behavior Modeling: Simple Example MOOC: Introduction to Discrete Choice Models

Introduces behavior modeling through a simple example, focusing on analyzing the market for electric cars.

Optimization and Simulation

Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.

Introduction to Behavior Modeling: Simple Example

Introduces behavior modeling through a simple example, focusing on choice modeling components and the analysis of the electric car market.

Detecting and Correcting Parameter Errors in Power Grids Log in to Mediaspace to watch this video

Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.

Detecting and Correcting Parameter Errors in Power Grids Log in to Mediaspace to watch this video

Explores methods for detecting and correcting parameter errors in power grids using statistical analysis and computational tools.