Bootstrap-basedmodel selection has been shown inmany 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...

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance...

Given the wealth of literature on the topic supported by solutions to practical problems, we would expect the bootstrap to be an off-the-shelf tool for signal processing problems as are maximum likelihood and least-squares methods. This is not the case, and we wonder why a signal processing...