Results 1 to 10 of about 37,560 (159)
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 +6 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
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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
James Stephen Marron, Birgit Grund
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The smoothing factor in breast intensity-modulated radiotherapy: dosimetric impact and a predictive model of plan acceptability [PDF]
Reports of Practical Oncology and RadiotherapyBACKGROUND: The smoothing factor is a key planning parameter in intensity-modulated radiotherapy that affects both plan quality and deliverability. This study investigates the dosimetric impact of SF in breast radiotherapy and introduces a logistic ...
Tamás Ungvári +8 more
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Resistant selection of the smoothing parameter for smoothing splines
Statistics and Computing, 2001 Robust automatic selection techniques for the smoothing parameter of a smoothing spline are introduced. They are based on a robust predictive error criterion and can be viewed as robust versions of Cp and cross-validation. They lead to smoothing splines which are stable and reliable in terms of mean squared error over a large spectrum of model ...
Eva Cantoni, Elvezio Ronchetti
exaly +3 more sources
Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
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Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
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Stats, 2021 Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently used in applied research to estimate unknown functions from noisy data ...
Lauren N. Berry, Nathaniel E. Helwig
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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method.
Hazriani Hazriani, Yuyun, Mashur Razak
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Two-dimensional NMR inversion based on fast norm smoothing method
Energy Geoscience, 2022 Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast,
Youlong Zou +5 more
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