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.
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Smoothing parameter and model selection for general smooth models (with discussion) [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.
Pya, Natalya +2 more
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Targeted smoothing parameter selection for estimating average causal effects [PDF]
Computational Statistics, 2013 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.
de Luna, Xavier, Häggström, Jenny
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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 S Marron
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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
Birgit Grund, Peter Hall, J. S. Marron
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A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia [PDF]
MethodsXSingle or simple exponential smoothing (SES) is a time series forecasting model popular among researchers due to its simplicity and ease of use. SES only requires one smoothing parameter, alpha, to control how quickly the influence of past observations ...
Azlan Abdul Aziz +4 more
doaj +2 more sources
The State Space Models Toolbox for MATLAB [PDF]
Journal of Statistical Software, 2011 State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy ...
Jyh-Ying Peng, John A. D. Aston
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HISAPS: High-order smoothing spline with automatic parameter selection and shape constraints
SoftwareXObtaining a good functional fit with noisy data is difficult. This is especially true when the derivative of the fitted function is needed, which is often the case in engineering applications. One solution is to use smoothing splines.
Peter H. Broberg +8 more
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Deconvolution Estimation in Measurement Error Models: The R Package decon [PDF]
Journal of Statistical Software, 2011 Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models.
Xiao-Feng Wang, Bin Wang
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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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