Mode choice analysis has received the most attention among discrete choice problems in travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. This paper investigates performance of mode choice analysis with classification methods decision trees, discriminant analysis and multinomial logit. Experi...

We propose a simple nonparametric mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program, and computationally attractive compared to alternative estimators for random coefficient models. For c...

Spatially varying coefficient (SVC) models are a type of regression model for spatial data where covariate effects vary over space. If there several covariates, natural question is which covariates have spatially effect and not. We present new variable selection approach Gaussian process-based SVC models. It relies on penalized maximum likelihood estimation (PMLE) allows both with respect to fi...

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people pr...