离散选择模型的重采样估计

IF 2.8 3区 经济学 Q1 ECONOMICS
Nicola Ortelli , Matthieu de Lapparent , Michel Bierlaire
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引用次数: 0

摘要

在离散选择建模中,从大型数据集中提取潜在的行为洞察力往往受到最大似然估计可扩展性差的限制。本文提出了一种简单、快速的数据集还原方法,这种方法专门用于保留数据集中原本存在的丰富观测数据,同时降低估计过程的计算复杂度。我们的方法称为 LSH-DR,它利用对位置敏感的哈希算法创建同质聚类,然后从中抽取具有代表性的观测值并进行加权。我们在一个真实世界的模式选择数据集上应用这种方法,证明了它的功效:结果表明,LSH-DR 生成的样本可以大大节省估计时间,同时以极小的代价保持估计效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resampling estimation of discrete choice models

In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that is specifically designed to preserve the richness of observations originally present in a dataset, while reducing the computational complexity of the estimation process. Our approach, called LSH-DR, leverages locality-sensitive hashing to create homogeneous clusters, from which representative observations are then sampled and weighted. We demonstrate the efficacy of our approach by applying it on a real-world mode choice dataset: the obtained results show that the samples generated by LSH-DR allow for substantial savings in estimation time while preserving estimation efficiency at little cost.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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