INFERENSI BAYESIAN DALAM WAVELET SHRINKAGE
In this paper, we discuss the Bayesian inference in wavelet nonparametric problems. In most areas of application, there is a need for a shrinkage procedure to (i) adapt to data and (ii) use prier information. The Bayesian paradigm provides a natural terrain for both of these...
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| Main Authors: | , |
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| 格式: | Article PeerReviewed |
| 語言: | English |
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Sekolah Pascasarjana UGM
2003
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/32969/1/11.pdf https://repository.ugm.ac.id/32969/ |
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| 機構: | Universitas Gadjah Mada |
| 語言: | English |
| 總結: | In this paper, we discuss the Bayesian inference in wavelet nonparametric problems. In most areas of application, there is a need for a shrinkage procedure to (i) adapt to data and (ii) use prier information. The Bayesian paradigm provides a natural terrain for both of these goals. •
In 1.oavelet domain, the Bayes rules §"(d) under the squared error loss function with selecti.on prior distribution 1r(fJ) for ()
whose symmetric property i.e TC(B) = rc(-B), in fact mjmic
'shrinkers', such that can be solved with wavelet shrinkage. In case the prior distribution for B with symmetric property and satisfies E(B) = 0, such that the shrinkage rules to wavelet coefficients
toward the prior mean can be solved.
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