Discrete Choice Analysis with Machine Learning Capabilities

no code implementations • 21 Jan 2021 • Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva

This paper discusses capabilities that are essential to models applied in policy analysis settings and the limitations of direct applications of off-the-shelf machine learning methodologies to such settings.

BIG-bench Machine Learning Discrete Choice Models

Sparse Covariance Estimation in Logit Mixture Models

no code implementations • 14 Jan 2020 • Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva

We propose a new estimator, called MISC, that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix.