Bayesian selection of primary resolution and wavelet bias functions for wavelet regression (Q626208)
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scientific article; zbMATH DE number 5855586
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Bayesian selection of primary resolution and wavelet bias functions for wavelet regression |
scientific article; zbMATH DE number 5855586 |
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Bayesian selection of primary resolution and wavelet bias functions for wavelet regression (English)
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22 February 2011
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A shrinkage rule for wavelet regression is proposed where the primary resolution \(m\) and the set of basis functions with non-zero coefficients are selected via a Bayesian approach. Non-informative (flat) priors for \(m\) and the number \(s\) of ``significant'' basis functions are given. The basis functions are rearranged in order of their ``importance'' and \(s\) ``most important'' functions are selected. The performance of the proposed algorithm is investigated via simulations.
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nonparametric regression
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Bayesian inference
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shrinkage
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