Bias-variance trade-off for prequential model list selection

Ernest Fokoue, Bertrand Clarke

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)813-833
Number of pages21
JournalStatistical Papers
Volume52
Issue number4
DOIs
StatePublished - Nov 2011

Keywords

  • Bayes model averaging
  • Bias-variance trade-off
  • Model list selection
  • Model selection
  • Online prediction
  • Prequential

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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