On Substantive Research Hypotheses, Conditional Independence Graphs and Graphical Chain Models
本文用图形表示变量间关系，将缺失连接解释为条件独立，提出图形链模型作为统一框架，帮助识别不同统计模型间的类比与等价，适用于数据分析。
SUMMARY Graphs consisting of points, and lines or arrows as connections between selected pairs of points, are used to formulate hypotheses about relations between variables. Points stand for variables, connections represent associations. When a missing connection is interpreted as a conditional independence, the graph characterizes a conditional independence structure as well. Statistical models, called graphical chain models, correspond to special types of graphs which are interpreted in this fashion. Examples are used to illustrate how conditional independences are reflected in summary statistics derived from the models and how the graphs help to identify analogies and equivalences between different models. Graphical chain models are shown to provide a unifying concept for many statistical techniques that in the past have proven to be useful in analyses of data. They also provide tools for new types of analysis.