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

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

Glucose Calibration Modeling in Blood with Spline Regression Approaching to Non-Invasive Tools

2018 ◽  
pp. 144-149
Author(s):  
Ria Hayatun Nur ◽  
Indahwati A ◽  
Erfiani A
Keyword(s):  
Diabetes Mellitus ◽  
Linear Regression ◽  
Good Health ◽  
Cubic Spline ◽  
The Body ◽  
Spline Regression ◽  
Linear Quadratic ◽  
Important Thing ◽  
Non Invasive

In this globalization era, health is the most important thing to be able to run various activities. Without good health, this will hinder many activities. Diabetes mellitus is one of the diseases caused by unhealty lifestyle.There are many treatments that can be done to prevent the occurrence of diabetes. The treatments are giving the insulin and also checking the glucose rate to the patients.Checking the glucose rate needs the tools which is safety to the body. This research want to develop non invasive tool which is safety and do not injure the patient. The purpose of this research is also finding the best model which derived from Linear, Quadratic, and Cubic Spline Regression. Some respondents were taking to get the glucose measuring by invasive and non invasive tools. It could be seen clearly that Spline Linear Regression was the best model than Quadratic and Cubic Spline Regression. It had 70% and 33.939 for R2 and RMSEP respectively.

Optic nerve sheath diameter changes at high altitude and in acute mountain sickness: meta-regression analyses

2020 ◽  
pp. bjophthalmol-2020-317717
Author(s):  
Tou-Yuan Tsai ◽  
George Gozari ◽  
Yung-Cheng Su ◽  
Yi-Kung Lee ◽  
Yu-Kang Tu
Keyword(s):  
Optic Nerve ◽  
High Altitude ◽  
Acute Mountain Sickness ◽  
Optic Nerve Sheath Diameter ◽  
Optic Nerve Sheath ◽  
Cochrane Library ◽  
Regression Analyses ◽  
Spline Regression ◽  
Nerve Sheath ◽  
Mountain Sickness

Background/aimsTo assess changes in optic nerve sheath diameter (ONSD) at high altitude and in acute mountain sickness (AMS).MethodsCochrane Library, EMBASE, Google Scholar and PubMed were searched for articles published from their inception to 31st of July 2020. Outcome measures were mean changes of ONSD at high altitude and difference in ONSD change between subjects with and without AMS. Meta-regressions were conducted to investigate the relation of ONSD change to altitude and time spent at that altitude.ResultsEight studies with 248 participants comparing ONSD from sea level to high altitude, and five studies with 454 participants comparing subjects with or without AMS, were included. ONSD increased by 0.14 mm per 1000 m after adjustment for time (95% CI: 0.10 to 0.18; p<0.01). Restricted cubic spline regression revealed an almost linear relation between ONSD change and time within 2 days. ONSD was greater in subjects with AMS (mean difference=0.47; 95% CI: 0.14 to 0.80; p=0.01; I2=89.4%).ConclusionOur analysis shows that ONSD changes correlate with altitude and tend to increase in subjects with AMS. Small study number and high heterogeneity are the limitations of our study. Further large prospective studies are required to verify our findings.

scholarly journals Monotone spline regression for accurate MTF measurement at low frequencies

2014 ◽  
Vol 22 (19) ◽  
pp. 22446 ◽  
Author(s):  
Zhongxing Zhou ◽  
Feng Gao ◽  
Huijuan Zhao ◽  
Lixin Zhang ◽  
Liqiang Ren ◽  
...  
Keyword(s):  
Spline Regression ◽  
Low Frequencies
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