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Strategic sampling for large choice sets in estimation and application

机译：估计和应用中大型选择集的战略抽样

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摘要

Many discrete choice contexts in transportation deal with large choice sets, including des tination, 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 a priori, the first iteration uses SRS among all available alter natives. The sampling scheme is implemented here for a variety of simulated MNL and mixed-MNL data sets, with results suggesting that the new sampling scheme provides sub stantial efficiency benefits. Thanks to reductions in estimation error, parameter estimates are more accurate, on average. Moreover, in the mixed MNL case, where SRS produces biased estimates (due to violation of the independence of irrelevant alternatives property), the new sampling scheme appears to effectively eliminate such biases. Finally, it appears that only a single iteration of the new strategy (following the initialization step using SRS) is needed to deliver the strategy's maximum efficiency gains.
机译：运输中的许多离散选择上下文涉及大量选择集，包括目的地，路线和车辆选择。具有大量替代方案的模型估计在计算上仍然很昂贵。在多项式对数（MNL）模型的情况下，通过简单随机抽样（SRS）限制估计中替代项的数量会产生一致的参数估计，但估计器效率会受到影响。在更通用的模型（例如混合MNL）的上下文中，通过SRS限制替代方案的数量会产生偏差的参数估计值。本文介绍了一种新的战略性抽样方案，该方案根据更新的选择概率估计按比例绘制替代方案。由于先验未知此类概率，因此第一次迭代在所有可用替代本机中使用SRS。此处针对各种模拟的MNL和混合MNL数据集实施了采样方案，结果表明，新的采样方案具有明显的效率优势。由于估计误差的减少，平均而言参数估计更加准确。此外，在混合MNL的情况下，SRS产生有偏差的估计（由于违反了无关选择属性的独立性），新的抽样方案似乎有效地消除了这种偏差。最终，似乎只需要对新策略进行一次迭代（在使用SRS的初始化步骤之后）即可实现该策略的最大效率提升。

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  • 来源
    《Transportation Research》 |2012年第3期|p.602-613|共12页
  • 作者

    Jason D. Lemp; Kara M. Kockelman;

  • 作者单位

    Cambridge Systematics, Inc., 9015 Mountain Ridge, Suite 210, Austin, TX 78759, United States;

    Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076, United States;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    multinomial logit model; strategic sampling scheme;

    机译：多项式logit模型战略抽样方案;

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