Bayesian Wavelet Regression on Curves With Application to a Spectroscopic Calibration Problem

By Philip J. Brown, Tom Fearn, Marina Vannucci. 2001

189 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/411117cbccbe38199284d2292ba89965e6afa722

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In Analytical Chemistry

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Citations in the corpus

Functional linear regression with derivatives (2006, methodology): “[24], we estimated also predictors after transforming the original ys into log ratios of the form z = log(y/(1 − y)).”
Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior (2016, methodology): “In the statistics literature, the Cereal and Multidrug data were both studied by Polson & Scott (2012) and Griffin & Brown (2013); and the Cookie data was studied by Brown et al. (2001) and Ghosh &”
BAYESIAN HYPER‐LASSOS WITH NON‐CONVEX PENALIZATION (2011, methodology): “The average MSE on the validation set (31 observations) is 0.0565 (94% explained), which is competitive with that achieved in Brown et al. (2001) via full MCMC and a ‘slaband-spike’ prior.”
Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data (2006, background): “The following example(1) is taken from [27] and is also discussed in [3].”
The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration (2005, background): “A survey of the recent literature indeed reveals an increasing use of wavelets in the analysis of spectroscopic signals, with empirical reports that this use decreases (sometimes) the prediction errors of multivariate regression algorithms [33–37].”
Functional Regression (2014, unknown): “Wavelets for Xi(t) and B(t): Although not explicitly describing their model as functional regression, Brown et al. (2001) introduced a Bayesian, wavelet-based approach for fitting FLMs to data on a common equally-spaced grid t.”
Bayesian Variable Selection Under Collinearity (2015, background): “Brown, Fearn, and Vannucci (2001) omitted the first 140 and last 49 of the available 700 wavelengths to reduce the computational burden because these were thought to contain little information.”
Space‐time modelling of trends in temperature series (2011, methodology): “…from the analysis of longitudinal models using wavelet decompositions to simplify the correlation structure of so-called functional data (e.g. Brown et al., 2001, Morris et al., 2003, Vannucci et al., 2003, Morris and Carroll, 2006, Morris et al., 2006). aThe State University bUniversity of…”