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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 of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it...
Corneal topography estimation that is based on the Placido disk principle relies on good quality of precorneal tear film and sufficiently wide eyelid (palpebral) aperture to avoid reflections from eyelashes. However, in practice, these conditions are not always fulfilled resulting in missing regions, smaller corneal coverage, and subsequently poorer estimates of corneal topography. Our aim was to...
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...
We propose a new resampling scheme that takes literally the concept of the non-parametric bootstrap in which new samples are generated from the empirical distribution function. The introduced resampling concept is totally heuristic, but already shows promising results when applied to model selection. We show that for a range of linear models, the proposed resampling scheme outperforms the classical...
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