Bias-variance trade-off for prequential model list selection

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 t...

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Vydáno v:	Statistical papers (Berlin, Germany) Ročník 52; číslo 4; s. 813 - 833
Hlavní autoři:	Fokoue, Ernest, Clarke, Bertrand
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer-Verlag 01.11.2011 Springer Nature B.V
Témata:	Approximation Bayesian analysis Bias Economic Theory/Quantitative Economics/Mathematical Methods Economics Finance Generators Insurance Lists Management Mathematical analysis Mathematical models Mathematics and Statistics Operations Research/Decision Theory Predictions Probability Theory and Stochastic Processes Regular Article Searching Statistics Statistics for Business Studies Variance Model list selection Model selection Online prediction Bias-variance trade-off Bayes model averaging Prequential
ISSN:	0932-5026, 1613-9798
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0932-5026 1613-9798
DOI:	10.1007/s00362-009-0289-6