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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 +5 more sources
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
core +3 more sources
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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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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