Wang, X and Walker, S G (2013) Full Bayesian wavelet inference with a nonparametric prior. Journal of Statistical Planning and Inference, 143 (1). pp. 55-62. ISSN 0378-3758.
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In this paper,we introduce a new Bayesian nonparametric model for estimating an unknown function in the presence of Gaussian noise.The proposed model involves a mixture of a point mass and an arbitrary (nonparametric) symmetric and unimodal distribution for modeling wavelet coefficients. Posterior simulation uses slice sampling ideas and the consistency under the proposed model is discussed. In particular, the method is shown to be computationally competitive with some of best Empirical wavelet estimation methods.
|Uncontrolled keywords:||Stick-breaking priors, slice sampling, Wavelet shrinkage, consistency|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Stephen Walker|
|Date Deposited:||03 Oct 2012 21:48|
|Last Modified:||16 Jan 2013 12:12|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/31233 (The current URI for this page, for reference purposes)|
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