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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Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Vol. 52; no. 4; pp. 813 - 833
Main Authors: Fokoue, Ernest, Clarke, Bertrand
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.11.2011
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-009-0289-6