We address an issue of model-searching based on a collection of marginal models under an assumption that the model is graphically decomposable. A main theme which is instrumental throughout the thesis is that decomposability is preserved between a model and its submodel and that a particular type of separators in the graph of model is found in a decomposable graphical model and in a collection of its submodels. These separators, which are called minimal connector sin the thesis, are a guideline for model combination. A theory for the guideline is laid out to the effect that we may use the minimal connectors for drawing a blueprint based on which a combined model is formed. This theoretic result is then carried over to a more larger set of hierarchical log-linear models by applying the concept of interaction graph. The result of thethesis is applied to real data for building a hierarchical log-linear model that can not be handled as a single model.
위계적 선형 로그모형과 그래프 모형과의 결합은 복잡한 자료에 대한 간단하면서 직관적인 해석을 제공해준다. 하지만 다량의 자료를 분석하는 경우에는 상당한 시간이 필요하다. 이를 해결하기 위해 본 논문에서는 조건부 선형로그모형 및 결합모형을 제안했다. 이 방법은 주어진 자료를 분할함으로써 더욱 쉽고 빠르게 모형을 찾는 방법을 보여주며 분할된 모형을 결합할 수 있는 조건을 제시한다.또한 실제 자료에 적용해 봄으로써 이 모형탐색 방법의 효율성을 확인했고 그에 따른 문제점을 제시했다.
