论文标题
通过离散选择模型近似选择数据
Approximating Choice Data by Discrete Choice Models
论文作者
论文摘要
我们获得了必要且充分的条件，在该条件下，随机循环离散选择模型（例如混合量模型）足够丰富，可以在选择集中任意良好地近似任何非参数随机效用模型。事实证明，条件是一组特征向量的仿射独立性。当条件失败时，导致某些无法紧密近似的随机效用模型，我们确定偏好和替换模式，这些模式具有挑战性地近似。我们还提出了算法来量化近似误差的幅度。
We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric random utility models arbitrarily well across choice sets. The condition turns out to be the affine-independence of the set of characteristic vectors. When the condition fails, resulting in some random utility models that cannot be closely approximated, we identify preferences and substitution patterns that are challenging to approximate accurately. We also propose algorithms to quantify the magnitude of approximation errors.
