Entropy 2011, 13(8), 1425-1445; doi:10.3390/e13081425
Article

A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model

1email, 2,* email and 2email
Received: 20 June 2011; in revised form: 15 July 2011 / Accepted: 20 July 2011 / Published: 2 August 2011
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: A new approach for estimating the aggregate hierarchical logit model is presented. Though usually derived from random utility theory assuming correlated stochastic errors, the model can also be derived as a solution to a maximum entropy problem. Under the latter approach, the Lagrange multipliers of the optimization problem can be understood as parameter estimators of the model. Based on theoretical analysis and Monte Carlo simulations of a transportation demand model, it is demonstrated that the maximum entropy estimators have statistical properties that are superior to classical maximum likelihood estimators, particularly for small or medium-size samples. The simulations also generated reduced bias in the estimates of the subjective value of time and consumer surplus.
Keywords: hierarchical logit model; lagrange multipliers; maximum entropy; maximum likelihood
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MDPI and ACS Style

Donoso, P.; De Grange, L.; González, F. A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model. Entropy 2011, 13, 1425-1445.

AMA Style

Donoso P, De Grange L, González F. A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model. Entropy. 2011; 13(8):1425-1445.

Chicago/Turabian Style

Donoso, Pedro; De Grange, Louis; González, Felipe. 2011. "A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model." Entropy 13, no. 8: 1425-1445.

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