Outlier‐Robust Bayesian Multinomial Choice Modeling
提出一种贝叶斯方法，用于稳健估计多项选择模型中的离群值，适用于相关或不相关的选择方案，通过厚尾拉普拉斯分布和收缩过程处理响应和杠杆点离群值，模拟和实证分析表明其优于非稳健方法，有助于定价决策。
Summary A Bayesian method for outlier‐robust estimation of multinomial choice models is presented. The method can be used for both correlated as well as uncorrelated choice alternatives and guarantees robustness towards outliers in the dependent and independent variables. To account for outliers in the response direction, the fat‐tailed multivariate Laplace distribution is used. Leverage points are handled via a shrinkage procedure. A simulation study shows that estimation of the model parameters is less influenced by outliers compared to non‐robust alternatives. An analysis of margarine scanner data shows how our method can be used for better pricing decisions. Copyright © 2015 John Wiley & Sons, Ltd.