A Simple Bayesian Procedure for Estimation in a Conjoint Model
提出一种结合自解释数据与联合数据的简单贝叶斯方法，用于估计个体层面的联合模型，相比普通最小二乘法可提升预测准确性，并通过初步实证验证。
The authors propose a simple Bayesian approach which combines self-explicated data with conjoint data for estimating individual-level conjoint models. Analytical results show that, with typical conjoint data, improvement may be expected over the estimation and prediction results obtained with ordinary least squares (OLS). The expected improvement in prediction is confirmed by pilot empirical results.