Model Selection to Enhance Prediction Performance in the Presence of Missing Data

Most nonlinear modelling approaches focus on solving a model selection problem with complete data, and in many cases original data are pre-processed with some nonlinear transforms such as Box-Tidwell and fractional polynomial transformation. Often these approaches can lead to models that are better than traditional models (for example, logistic model and quadratic model). However, in the case of missing data, it is not easy to predict the relationship between the predictor and dependent variables; traditional nonlinear models in some cases of missing data analysis give poor results. This paper explains nonlinear model selection techniques for missing data. It includes an overview of nonlinear model selection with complete data, and provides accessible descriptions of Box-Tidwell and fractional polynomial methods for model selection. In particular, this paper focuses on a fractional polynomial method for nonlinear modelling in cases of missing data and presents analysis examples to illustrate performance of the method.