Article ID: | iaor20121393 |
Volume: | 46 |
Issue: | 3 |
Start Page Number: | 602 |
End Page Number: | 613 |
Publication Date: | Mar 2012 |
Journal: | Transportation Research Part A |
Authors: | Lemp Jason D, Kockelman Kara M |
Keywords: | statistics: regression, statistics: sampling |
Many discrete choice contexts in transportation deal with large choice sets, including destination, route, and vehicle choices. Model estimation with large numbers of alternatives remains computationally expensive. In the context of the multinomial logit (MNL) model, limiting the number of alternatives in estimation by simple random sampling (SRS) yields consistent parameter estimates, but estimator efficiency suffers. In the context of more general models, such as the mixed MNL, limiting the number of alternatives via SRS yields biased parameter estimates. In this paper, a new, strategic sampling scheme is introduced, which draws alternatives in proportion to updated choice‐probability estimates. Since such probabilities are not known