Smoothing spline regression estimation based on real and artificial data

Metrika ◽  
2015 ◽  
Vol 78 (6) ◽  
pp. 711-746 ◽  
Author(s):  
Dmytro Furer ◽  
Michael Kohler
Keyword(s):  
Smoothing Spline ◽  
Regression Estimation ◽  
Spline Regression ◽  
Artificial Data

Fixed-design regression estimation based on real and artificial data

2013 ◽  
Vol 25 (1) ◽  
pp. 223-241 ◽  
Author(s):  
Dmytro Furer ◽  
Michael Kohler ◽  
Adam Krzyżak
Keyword(s):  
Regression Estimation ◽  
Artificial Data ◽  
Fixed Design ◽  
Fixed Design Regression

scholarly journals PEMILIHAN PARAMETER PENGHALUS DALAM REGRESI SPLINE LINIER

2011 ◽  
Vol 3 (1) ◽  
pp. 9
Author(s):  
Agustini Tripena Br. Sb.
Keyword(s):  
Mean Square Error ◽  
Cross Validation ◽  
Selection Method ◽  
Regression Estimation ◽  
Mean Square ◽  
Spline Regression ◽  
Selection Methods ◽  
The Mean ◽  
Smoothing Parameters ◽  
Selection Of

This paper discusses aselection of smoothing parameters for the linier spline regression estimation on the data of electrical voltage differences in the wastewater. The selection methods are based on the mean square errorr (MSE) and generalized cross validation (GCV). The results show that in selection of smooting paranceus the mean square error (MSE) method gives smaller value , than that of the generalized cross validatio (GCV) method. It means that for our data case the errorr mean square (MSE) is the best selection method of smoothing parameter for the linear spline regression estimation.

scholarly journals PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

2018 ◽  
Vol 7 (3) ◽  
pp. 259
Author(s):  
NI LUH SUKERNI ◽  
I KOMANG GDE SUKARSA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Regression Model ◽  
Cross Validation ◽  
Estimation Method ◽  
Growth Patterns ◽  
Regression Estimation ◽  
Spline Regression ◽  
Quadratic Spline ◽  
Nonparametric Regression Estimation ◽  
Weight Growth ◽  
High Flexibility

The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient  equals to 0,954609.

Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve

2018 ◽  
Vol 43 (3) ◽  
pp. 225-232 ◽  
Author(s):  
Rajesh Wadhvani ◽  
Sanyam Shukla
Keyword(s):  
Power System ◽  
Wind Turbine ◽  
Wind Power ◽  
Polynomial Regression ◽  
Technical Specification ◽  
Smoothing Spline ◽  
Power Curve ◽  
Spline Regression ◽  
Regression Techniques ◽  
Weighted Polynomial Regression

Wind turbine power curve provides technical specification of the wind turbine in the form of nominal wind power readings. This information may used to monitor the performance of the power system, estimate the power produced by the turbine, optimize the operational cost, and improve the reliability of the power system. However, this information is not sufficient to accomplish these tasks. To accomplish these tasks, the accurate modeling of the wind power curve is required. In this article, various curve fitting techniques, namely polynomial regression, locally weighted polynomial regression, spline regression, piecewise polynomial regression, and smoothing spline, have been applied to model the power curve of wind turbine. All these techniques have been used to model the power curve on National Renewable Energy Laboratory (NREL) 2012 dataset with site-id 124693.

Isotonic Smoothing Spline Regression

2008 ◽  
Vol 17 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Xiao Wang ◽  
Feng Li
Keyword(s):  
Smoothing Spline ◽  
Spline Regression
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