A unified approach to conjoint analysis models
A unified approach to conjoint analysis models
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DOI：
10.1198/016214502388618410
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发表时间：
2002-09-01
影响因子：
3.7
通讯作者：
Bradlow, ET
中科院分区：
文献类型：
--
作者：
Marshall, P;Bradlow, ET
我们提出了一种使用贝叶斯框架的联合分析模型的统一方法。一个数据来源被用来形成部分价值的先验分布，而评级尺度、排名、离散选择或恒定和尺度下的全面评估构成了可能性数据(“一个模型适用于所有”)。用于联合分析的标准现有模型。在文献中被认为是。成为所提出的规范的特例，并给出了使用多个数据源的收益的显式公式。我们在一个联合分析数据集上演示了我们的方法，该数据集包含Krieger，Green和Uesh最初收集和描述的新车的自我解释的评估和常量和配置文件数据。我们的经验发现是“混合的”，因为对于一些样本外的预测指标，我们的贝叶斯方法优于使用仅有个人资料或仅自我解释的数据。从其他方面来看，情况并非如此。我们的发现表明，自我解释的数据是否添加了超过简档数据的信息的主要决定因素是校准和验证数据格式之间的不一致程度。具体地说，当为两个来源收集相同类型的数据时。自我解释的数据增加得更少。反之亦然。我们工作的另一个贡献(考虑到我们方法的一般性，也是很容易实现的)是，我们将数据与固定总和、排名和二元选择模型进行匹配，从而允许我们在获取数据并转换其规模时推断信息的“变化”(这是一种常见的做法)。仿真研究表明了该方法的可行性。在该模型下，考虑了一种适用于结果度量形式的简单Gibbs采样器模拟方案，该方案使用数据扩充和Metropolis抽样进行推理。
We present a unified approach to conjoint analysis models using a Bayesian framework. One data source is used to form a prior distribution for the partworths, whereas full-profile evaluations under a rating scale, ranking, discrete choice, or constant-sum scale constitute the likelihood data ("one model fits all"). Standard existing models for conjoint analysis. considered in the literature. become particular cases of the proposed specification, and explicit formulas for the gains of using multiple Sources of data are presented. We demonstrate our method on a conjoint analysis dataset containing both self-explicated evaluations and constant-sum profile data on new automobiles originally collected and described by Krieger, Green, and Umesh. Our empirical findings are "mixed" in that for some out-of-sample predictive measures our Bayesian approach is superior to using profile-only or self-explicated-only data. and for other measures it is not. Our findings suggest that the primary determinant as to whether self-explicated data add information above and beyond the profile data is the degree of incongruity between the calibration and validation data formats. Specifically, when the same type of data are collected for both sources. self-explicated data add less. and vice versa. A further contribution of our Work (and one that is easily implemented, given the general nature of our approach) is that we take our data and fit constant sum, ranking, and binary choice models to it, allowing, us to infer the "change" in information when taking data and transforming its scale (a common practice). A simulation study indicates the viability of this approach. A simple Gibbs sampler simulation scheme adapted to the form of the outcome measure, using data augmentation and Metropolis sampling, is considered for inference under the model.