Due to the surge in the amount of data that are being collected, analysts are increasingly faced with very large data sets. Estimation of sophisticated discrete choice models (such as Mixed Logit models) based on these typically large data sets can be computationally burdensome, or even infeasible. Hitherto, analysts tried to overcome these computational burdens by reverting to less computationally demanding choice models or by taking advantage of the increase in computational resources. In this paper we take a different approach: we develop a new method called Sampling of Observations (SoO) which scales down the size of the choice data set, prior to the estimation. More specifically, based on information-theoretic principles this method extracts a subset of observations from the data which is much smaller in volume than the original data set, yet produces statistically nearly identical results. We show that this method can be used to estimate sophisticated discrete choice models based on data sets that were originally too large to conduct sophisticated choice analysis.

中文翻译：

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基于信息论的观察样本

由于收集的数据量激增，分析师越来越多地面临着非常庞大的数据集。基于这些通常较大的数据集估算复杂的离散选择模型（例如Mixed Logit模型）可能会在计算上造成负担，甚至不可行。迄今为止，分析人员试图通过恢复到对计算要求较低的选择模型或通过利用计算资源的增加来克服这些计算负担。在本文中，我们采用了另一种方法：我们开发了一种称为观察样本（SoO）的新方法，该方法在进行估计之前会缩小选择数据集的大小。更具体地说，根据信息理论原理，该方法从数据中提取观测的子集，其数量远小于原始数据集，但产生的统计结果几乎相同。我们表明，该方法可用于基于最初太大而无法进行复杂选择分析的数据集估计复杂的离散选择模型。