A decision theoretic approach to wavelet regression on curves with a high number of regressors

Vannucci, Marina and Brown, Philip J. and Fearn, T. (2003) A decision theoretic approach to wavelet regression on curves with a high number of regressors. Statistical Planning and Inference, 112 (1-2). pp. 195-212. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1016/S0378-3758(02)00333-6

Abstract

Here we consider a possibly multivariate regression setting where data arise as curves and where the number of predictors greatly exceeds the number of observations. We present typical applications in spectral calibration. We employ wavelets and transform curves into sets of wavelet coefficients describing local features of the spectra. We then apply a Bayesian decision theory approach to select those coefficients that predict well the response. The method requires cost specifications and we employ cost functions that depend on the wavelet scale. Stochastic optimization methods are needed to find optimal subsets. We investigate both simulated annealing and genetic algorithms.

Item Type: Article
Uncontrolled keywords: Bayes methods; Decision theory; Regression; Simulated annealing; Genetic algorithms; Wavelet transforms; Near-infrared spectroscopy
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
Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
Depositing User: Philip J Brown
Date Deposited: 28 May 2009 06:52
Last Modified: 13 May 2014 11:12
Resource URI: http://kar.kent.ac.uk/id/eprint/8144 (The current URI for this page, for reference purposes)
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