This proposed project has three primary objectives. Objective 1 is to develop improved strategies for fitting more accurate classification and regression tree (i.e., CART) models. Objective 2 is to develop a formal framework to allow statistical inference on tree models. Objective 3 is to develop and distribute public-domain software that will allow applied data analysts to implement the methods we develop in the first two objectives. To meet these objectives we will integrate statistical and computational machine learning approaches. We believe our work can have a significant impact in biomedical data analysis by combining the strengths of statistics for developing objective criteria for model selection and for providing a framework for assessing and quantifying uncertainty associated with a model, with the strengths of machine learning for fitting models to large and complex datasets.
|Zhang, W; Shannon, W D; Duncan, J et al. (2006) Expression of drug pathway proteins is independent of tumour type. J Pathol 209:213-9|
|Culverhouse, Robert; Klein, Tsvika; Shannon, William (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27:141-52|
|Shannon, W D; Province, M A; Rao, D C (2001) Tree-based recursive partitioning methods for subdividing sibpairs into relatively more homogeneous subgroups. Genet Epidemiol 20:293-306|