Abstract Artificial Neural Networks (ANNs) are increasingly used for discrete choice analysis. But, at present, it is unknown what sample size requirements are appropriate when using ANNs in this particular context. This paper fills this knowledge gap: we empirically establish a rule-of-thumb for ANN-based discrete choice analysis based on analyses of synthetic and real data. To investigate the effect of complexity of the data generating process on the minimum required sample size, we conduct extensive Monte Carlo analyses using a series of different model specifications with different levels of model complexity, including RUM and RRM models, with and without random taste parameters. Based on our analyses we advise to use a minimum sample size of fifty times the number of weights in the ANN; it should be noted, that the number of weights is generally much larger than the number of parameters in a discrete choice model. This rule-of-thumb is considerably more conservative than the rule-of-thumb that is most often used in the ANN community, which advises to use at least ten times the number of weights.

中文翻译：

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您的数据集足够大吗？使用人工神经网络进行离散选择分析时的样本量要求

摘要人工神经网络（ANN）越来越多地用于离散选择分析。但是，目前尚不清楚在这种特定情况下使用ANN时适合哪种样本量要求。本文填补了这一知识空白：我们基于对合成数据和真实数据的分析，为基于ANN的离散选择分析建立了经验法则。为了研究数据生成过程的复杂性对所需最小样本量的影响，我们使用一系列具有不同模型复杂度的不同模型规范（包括RUM和RRM模型，带有或不带有随机味觉参数）进行了广泛的蒙特卡洛分析。根据我们的分析，我们建议使用ANN中权重数量的50倍的最小样本量；应该指出的是，权数通常比离散选择模型中的参数数大得多。与ANN社区中最常用的经验法则相比，该经验法则要保守得多，后者建议使用至少十倍的权重。