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Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy. [PDF]
Comput Stat Data Anal, 2010 Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. We compare various robust smoothing methods for estimating fluorescence emission spectra and data driven methods for the ...
Lee JS, Cox DD.
europepmc +7 more sources
Smoothing Parameter and Model Selection for General Smooth Models [PDF]
Journal of the American Statistical Association, 2016 This paper discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to ...
Simon N Wood, Benjamin Säfken
exaly +7 more sources
Smoothing Parameter Selection in two Frameworks for Penalized Splines [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2013 SummaryThere are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated by minimizing criteria that approximate the average mean-squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a
T. Krivobokova
semanticscholar +7 more sources
Loss and risk in smoothing parameter selection [PDF]
Journal of Nonparametric Statistics, 1994 For several years there has been debate over the relative merits of loss and risk as measures of the performance of nonparametric density estimators. In the way that this debate has dealt with risk, it has largely ignored the fact that any practical bandwidth selection rule must produce a random bandwidth. Existing theory for risk of density estimators
Birgit Grund, J S Marron
exaly +3 more sources
Targeted smoothing parameter selection for estimating average causal effects [PDF]
Computational Statistics, 2014 The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit ...
Jenny Häggström, Xavier de Luna
semanticscholar +6 more sources
Smoothing Parameter Selection for a Class of Semiparametric Linear Models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2009 SummarySpline-based approaches to non-parametric and semiparametric regression, as well as to regression of scalar outcomes on functional predictors, entail choosing a parameter controlling the extent to which roughness of the fitted function is penalized.
Reiss, Philip T., Ogden, R. Todd
semanticscholar +3 more sources
Automatic Smoothing Parameter Selection: A Survey [PDF]
Empirical Economics, 1988 This is a survey of recent developments in smoothing parameter selection for curve estimation. The first goal of this paper is to provide an introduction to the methods available, with discussion at both a practical and also a nontechnical level, including comparison of methods.
J. Marron
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Empirical Smoothing Parameter Selection in Adaptive Estimation
The Annals of Statistics, 1992 The paper deals with selecting the smoothing parameter involved in the construction of adaptive estimates for the symmetric location model. In \textit{P. J. Bickel's} [Ann. Stat. 10, 647-671 (1982; Zbl 0489.62033)] original paper on adaptivity a necessary condition for adaption was given.
K. Jin
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Empirical Functionals and Efficient Smoothing Parameter Selection
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1992 SUMMARY A striking feature of curve estimation is that the smoothing parameter ĥ 0, which minimizes the squared error of a kernel or smoothing spline estimator, is very difficult to estimate. This is manifest both in slow rates of convergence and in high variability of standard methods such as cross-validation.
Peter Hall, Iain Johnstone
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Criteria for selecting the Paganin-filter reconstruction parameter in X-ray phase-contrast tomography [PDF]
Journal of Synchrotron RadiationThe Paganin filter, widely employed in propagation-based phase-contrast X-ray computed tomography with synchrotron light, attenuates phase effects and suppresses high-frequency noise under the assumption that samples have a constant β/δ ratio (these ...
Eduardo X. Miqueles +3 more
doaj +2 more sources