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A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model
Laboratorio de Modelamiento del Transporte y Uso del Suelo (LABTUS), Departamento de Ingeniería Civil, Universidad de Chile, Santiago PO Box 10-D, Chile
Escuela de Ingeniería Civil Industrial, Universidad Diego Portales, Santiago, 8370179, Chile
* Author to whom correspondence should be addressed.
Received: 20 June 2011; in revised form: 15 July 2011 / Accepted: 20 July 2011 / Published: 2 August 2011
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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Cite This Article
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.
Donoso P, De Grange L, González F. A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model. Entropy. 2011; 13(8):1425-1445.
Donoso, Pedro; De Grange, Louis; González, Felipe. 2011. "A Maximum Entropy Estimator for the Aggregate Hierarchical Logit Model." Entropy 13, no. 8: 1425-1445.