Vannucci, M. and Brown, P.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.
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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.
|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:||14 Jan 2010 14:30|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/8144 (The current URI for this page, for reference purposes)|
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