Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule
In this paper random utility maximization based on maximization of correct classification of the choice decisions over a given data set is considered. It is shown that if the disturbance vector in the random utility model is independent and identically distributed, then preference determination based on the most probable alternative reduces to deterministic utility maximization. As a consequence of the above equivalence, the form of the error distribution (normal, Weibull, uniform etc.) plays no role in the determination of the preferred alternative. Parameter estimation under the most probable alternative rule is carried out using two methods. The first is based on the solution of an appropriately defined system of linear inequalities and the second one is based on the function optimization of a newly proposed function, whose optimum is achieved when the number of correctly classified individuals is maximized. The ability to use these algorithms in the framework of pattern recognition and machine learning is pointed out. Simulations and a real case study involving intercity travel behavior are employed to assess the proposed methods.
random utility model pattern recognition decision analysis discrete choice models logit model