Bias-variance trade-off for prequential model list selection |
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Authors: | Ernest Fokoue Bertrand Clarke |
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Institution: | 1.Center for Quality and Applied Statistics,Rochester Institute of Technology,Rochester,USA;2.Department of Medicine,University of Miami,Miami,USA;3.Department of Epidemiology and Public Health,University of Miami,Miami,USA;4.Center for Computational Sciences,University of Miami,Miami,USA |
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Abstract: | The prequential approach to statistics leads naturally to model list selection because the sequential reformulation of the
problem is a guided search over model lists drawn from a model space. That is, continually updating the action space of a
decision problem to achieve optimal prediction forces the collection of models under consideration to grow neither too fast
nor too slow to avoid excess variance and excess bias, respectively. At the same time, the goal of good predictive performance
forces the search over good predictors formed from a model list to close in on the data generator. Taken together, prequential
model list re-selection favors model lists which provide an effective approximation to the data generator but do so by making
the approximation match the unknown function on important regions as determined by empirical bias and variance. |
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Keywords: | |
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