Lectures related to Discrete Panel Data

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

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

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

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Mixture Models: Simulation-based Estimation

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

Maximum Likelihood: Inference and Model Comparison

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

Panel data: dynamic model with panel effects MOOC: Selected Topics on Discrete Choice

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.

Panel data: static model MOOC: Selected Topics on Discrete Choice

Introduces the static model for panel data analysis and discusses its limitations.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Hidden Markov Models (HMM): Theory

Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Quantifying Statistical Dependence

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

EM Algorithm: Lite-bulbs Lifetime Estimation

Explains the EM algorithm for estimating lite-bulbs lifetime and discusses its application to heavy-bulbs.

Yule Walker Equations: Efficient Implementation and Correlation Analysis Log in to Mediaspace to watch this video

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing Log in to Mediaspace to watch this video

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Frame Synchronization: Systematic Derivation with Likelihood Test Log in to Mediaspace to watch this video

Covers the systematic derivation of a synchronization algorithm with a generalized likelihood test in wireless receivers.

Modeling Neurobiological Signals: Markov Chains Log in to Mediaspace to watch this video

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.