On the computer intensive methods in model selection

Bootstrap-based model selection has been shown in many practical instances to be superior to classical methods such the AIC and MDL. This is particularly noticeable when the distribution of modelling noise is unknown and/or when the available data samples are small. One of the main problems of using bootstrap model selection with real data is the necessity of tuning the residual scaling parameter or estimating the length of a sub-sample. Recently, we have developed a new hook and loop (HL) resampling plane, in which the scaling of the residuals is avoided. Here, we compare the performance of the range of resampling planes that can be used in the context of model selection and show that the HL-based model selection is superior to its predecessors. Moreover, in the context of fitting parametric models to corneal data measured by videokeratoscopes, the HL provides results that are consistent with clinical expectations.