A Single Ideal Point Model for Market Structure Analysis
提出一种概率多维尺度模型，通过引入依赖抽样假设克服单理想点模型的数学不确定性，从而仅凭偏好数据即可估计单一市场的产品空间图，并可结合相似性数据联合估计。
Computing unsegmented product maps from preference data by means of single ideal point models is commonly thought to be impossible because of indeterminacy problems. The authors show that this mathematical indeterminacy can be overcome by incorporating dependent sampling assumptions into a probabilistic multidimensional scaling (MDS) model. As a result, product space maps can be estimated for single markets from preference data alone. If desired, dissimilarity data can be combined with preference data to produce jointly estimated product space maps. The authors illustrate the advantages of the proposed approach with real and simulated data. They also make comparisons to both internal and external deterministic models. The results are favorable.