Discrete choice modeling with anonymized data

Modeling Discrete Choice with Uncertain Data: an Augmented Mnl Estimator

When estimating the value of natural resources, the applied analyst must often work with noisy or otherwise imprecise measures of both dependent and independent variables. To help control for this uncertainty, this article introduces a multinomial logit model (MNL) that uses ancillary information to control for uncertainty in observed choices and in the attributes of a respondent’s choice set. ...

Discrete Choice Modeling

Do the results change substantially when the restriction is imposed? Does the estimate of ρ change? The hypothesis of interest is H0: β1 = β2. You can test this hypothesis using these models with a likelihood ratio test. Compute twice the difference in the log likelihoods. What are the degrees of freedom for the test? What is the 95% critical value from the chi squared table? (It is also possib...

Discrete Choice Modeling

Discrete Choice Modeling for Transportation

Acknowledgements: Justin Tobias provided many useful references and comments on the third section of this paper. provided many useful comments and references. Of course, none of these people are responsible for any of the errors or omissions in this paper.

Active Preference Learning with Discrete Choice Data

We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an individual in order to find the item that they value highly in as few trials as possible, and exploits quirks of human psychology to minimize time and cognitive burden. To do this, our algorithm maximizes the expected...