Discrete choice experiments are frequently used to quantify consumer preferences by having respondents choose between different alternatives. Choice experiments involving mixtures of ingredients have been largely overlooked in the literature, even though many products and services can be described as mixtures of ingredients. As a consequence, little research has been done on the optimal design of choice experiments involving mixtures. The only existing research has focused on D-optimal designs, which means that an estimation-based approach was adopted. However, in experiments with mixtures, it is crucial to obtain models that yield precise predictions for any combination of ingredient proportions. This is because the goal of mixture experiments generally is to find the mixture that optimizes the respondents' utility. As a result, the I-optimality criterion is more suitable for designing choice experiments with mixtures than the D-optimality criterion because the I-optimality criterion focuses on getting precise predictions with the estimated statistical model. In this paper, we study Bayesian I-optimal designs, compare them with their Bayesian D-optimal counterparts, and show that the former designs perform substantially better than the latter in terms of the variance of the predicted utility.
翻译:对消费者的偏好,经常使用分辨选择实验,让受访者在不同的替代品中作出选择。涉及成分混合物的选择实验在文献中大都被忽视,尽管许多产品和服务可以被描述为成分混合物的混合物。因此,对涉及混合物的选择实验的最佳设计研究很少。唯一的现有研究集中于D-最佳设计,这意味着采用了基于估计的方法。然而,在对混合物的实验中,关键是要获得能够准确预测成份比例的任何组合的模型。这是因为混合物试验的目标通常是找到能够优化受访者效用的混合物。因此,I-最佳性标准比D-最佳性标准更适合于设计与混合物的选择实验,因为I-最佳性标准侧重于与估计的统计模型取得精确预测。在本文中,我们研究了Bayesian I-最佳设计,将其与Bayesian D-最佳对应方进行比较,并表明在预测效用的差异方面前一种设计比后者要好得多。