  In this paper we introduce a new model building algorithm called
self-validating ensemble modeling or SVEM. The method enables the fitting of
validated predictive models to the relatively small data sets typically
generated from designed experiments where prediction is the desired outcome
which is often the case in Quality by Design studies in bio-pharmaceutical
industries. In order to fit validated predictive models, SVEM uses a unique
weighting scheme applied to the responses and fractional weighted bootstrapping
to generate a large ensemble of fitted models. The weighting scheme allows the
original data to serve both as a training set and validation set. The method is
very general in application and works with most model selection algorithms.
Through extensive simulation studies and a case study we demonstrate that SVEM
generates models with lower prediction error as compared to more traditional
statistical approaches that are based on fitting a single model.
