An Exponential-Family Multidimensional Scaling Mixture Methodology
提出一种指数族多维尺度混合方法（STUNMIX），用于分析被试对刺激的偏好/选择，可同时估计刺激位置和理想点，支持多种数据类型，并允许协变量重参数化。
Abstract A multidimensional scaling methodology (STUNMIX) for the analysis of subjects' preference/choice of stimuli that sets out to integrate the previous work in this area into a single framework, as well as to provide a variety of new options and models, is presented. Locations of the stimuli and the ideal points of derived segments of subjects on latent dimensions are estimated simultaneously. The methodology is formulated in the framework of the exponential family of distributions, whereby a wide range of different data types can be analyzed. Possible reparameterizations of stimulus coordinates by stimulus characteristics, as well as of probabilities of segment membership by subject background variables, are permitted. The models are estimated in a maximum likelihood framework. The performance of the models is demonstrated on synthetic data, and robustness is investigated. An empirical application is provided, concerning intentions to buy portable telephones. KEY WORDS: Concomitant variable modelEM algorithmMaximum likelihoodUnfolding