Sampling of alternatives in Multivariate Extreme Value (MEV) models

Sampling of alternatives in Multivariate Extreme Value (MEV) models

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Article ID: iaor20131302
Volume: 48
Start Page Number: 31
End Page Number: 52
Publication Date: Feb 2013
Journal: Transportation Research Part B
Authors: ,
Keywords: statistics: sampling
Abstract:

We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Multivariate Extreme Value (MEV) models, extending a previous result for Logit. We illustrate the methodology and study the finite sample properties of the estimators using Monte Carlo experimentation and real data on residential location choice from Lisbon, Portugal. Experiments show that the proposed methodology is practical, that it outperforms the uncorrected model, and that it yields acceptable results, even for relatively small samples of alternatives. The paper finishes with a synthesis and an analysis of the impact, limitations and potential extensions of this research.

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