V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction [PDF]
Sensors, 2021 Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty ...
Zhanglong Cao +4 more
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An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples. [PDF]
Biometrika, 2021 Summary Large samples are generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyse such large samples because of high computational costs.
Sun X, Zhong W, Ma P.
europepmc +2 more sources
Novel Information-Driven Smoothing Spline Linearization Method for High-Precision Displacement Sensors Based on Information Criterions [PDF]
Sensors, 2023 A noise-resistant linearization model that reveals the true nonlinearity of the sensor is essential for retrieving accurate physical displacement from the signals captured by sensing electronics.
Wen-Hao Zhang +5 more
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Application of Smoothing Spline in Determining the Unmanned Ground Vehicles Route Based on Ultra-Wideband Distance Measurements [PDF]
Sensors, 2022 Unmanned ground vehicles (UGVs) are technically complex machines to operate in difficult or dangerous environmental conditions. In recent years, there has been an increase in research on so called “following vehicles”. The said concept introduces a guide—
Łukasz Rykała +3 more
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Defining window-boundaries for genomic analyses using smoothing spline techniques. [PDF]
Genet Sel Evol, 2015 High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise ...
Beissinger TM +4 more
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Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression
Journal of Mathematics, 2020 So far, most of the researchers developed one type of estimator in nonparametric regression. But in reality, in daily life, data with mixed patterns were often encountered, especially data patterns which partly changed at certain subintervals, and some ...
Ni Putu Ayu Mirah Mariati +2 more
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Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA. [PDF]
Stat, 2013 Quantile regression provides a more thorough view of the effect of covariates on a response. Non‐parametric quantile regression has become a viable alternative to avoid restrictive parametric assumption.
Lin CY, Bondell H, Zhang HH, Zou H.
europepmc +2 more sources
Smoothing Spline ANOVA Models: R Package gss
Journal of Statistical Software, 2014 This document provides a brief introduction to the R package gss for nonparametric statistical modeling in a variety of problem settings including regression, density estimation, and hazard estimation.
Chong Gu
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Principle of Duality in Cubic Smoothing Spline
Mathematics, 2020 Fitting a cubic smoothing spline is a typical smoothing method. This paper reveals a principle of duality in the penalized least squares regressions relating to the method.
Hiroshi Yamada
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Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model [PDF]
Journal of Sciences, Islamic Republic of Iran, 2020 Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The
Anna Islamiyati
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