Results 1 to 10 of about 34,984 (259)

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   +5 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
J S Marron, Birgit Grund
exaly   +2 more sources

Criteria for selecting the Paganin-filter reconstruction parameter in X-ray phase-contrast tomography [PDF]

Journal of Synchrotron Radiation
The 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, Everton L. de Oliveira, Murilo Carvalho, David M. Paganin   +3 more
doaj   +2 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
doaj   +1 more source

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 , Bahadir Yuzbasi , Dursun Aydin   +2 more
doaj   +1 more source

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 ...
Wood, Simon N., Pya, Natalya, Säfken, Benjamin   +2 more
openaire   +3 more sources

Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines

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
doaj   +1 more source

A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting

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
doaj   +1 more source

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, Jun Li, Song Hu, Junlei Su, Mi Liu, Jun Zhang   +5 more
doaj   +1 more source

On the Number of Independent Pieces of Information in a Functional Linear Model with a Scalar Response

Stats, 2020
In a functional linear model (FLM) with scalar response, the parameter curve quantifies the relationship between a functional explanatory variable and a scalar response.
Eduardo L. Montoya
doaj   +1 more source