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.

scholarly journals ANALISIS REGRESI NONPARAMETRIK SPLINE MULTIVARIAT UNTUK PEMODELAN INDIKATOR KEMISKINAN DI INDONESIA

2016 ◽  
Vol 5 (3) ◽  
pp. 111 ◽  
Author(s):  
DESAK AYU WIRI ASTITI ◽  
I WAYAN SUMARJAYA ◽  
MADE SUSILAWATI
Keyword(s):  
Nonparametric Regression ◽  
Cross Validation ◽  
Social Economy ◽  
Estimation Method ◽  
Regression Method ◽  
Regression Estimation ◽  
Multivariate Spline ◽  
Nonparametric Regression Estimation ◽  
Poverty Indicators ◽  
The Relationship

The aim of this study is to obtain statistics models which explain the relationship between variables that influence the poverty indicators in Indonesia using multivariate spline nonparametric regression method. Spline is a nonparametric regression estimation method that is automatically search for its estimation wherever the data pattern move and thus resulting in model which fitted the data. This study, uses data from survey of Social Economy National (Susenas) and survey of Employment National (Sakernas) of 2013 from the publication of the Central Bureau of Statistics (BPS). This study yields two models which are the best model from two used response variables. The criterion uses to select the best model is the minimum Generalized Cross Validation (GCV). The best spline model obtained is cubic spline model with five optimal knots.

scholarly journals MODEL REGRESI NONPARAMETRIK SPLINE TRUNCATED PADA JUMLAH KASUS TUBERKULOSIS DI PROVINSI BALI TAHUN 2016

2018 ◽  
Vol 7 (3) ◽  
pp. 211 ◽  
Author(s):  
NI PUTU RINA ANGGRENI ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Regression Model ◽  
Nonparametric Regression ◽  
Spline Function ◽  
Cross Validation ◽  
Spline Regression ◽  
Point Selection ◽  
Polynomial Models ◽  
The Government ◽  
High Flexibility

Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis. Based on data from the health office of Bali Province, in 2015 tuberculosis cases found 0,96%, while in 2016 tuberculosis cases increase to 1,05%. This research used truncated spline nonparametric regression to model tuberculosis cases in Bali Province in 2016. This method was used because truncated spline has high flexibility compared to other polynomial models. The truncated spline function has a connecting point called knots. The best estimation of truncated spline regression model is obtained from optimal knot point selection by calculating minimum generalized cross validation. The estimated truncated model is linear with one knot point with determination coefficient equals to 70,48 %. In addition, it is also found in order to reduce tuberculosis cases the government of Bali Province should increase percentage of family who lives clean and healthy.

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 271-272 ◽  
pp. 932-935
Author(s):  
Hong Ying Hu ◽  
Wen Long Li ◽  
Feng Qiang Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Regression Function ◽  
Regression Estimation ◽  
Function Estimation ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate trend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet regression estimation method, a new regression function estimation method based on EMD is presented. The simulation proves the advantages of the approach with easy computation and more accurate result.

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.

Truncated Spline Regression to Estimate Regression Curve of Strontium Titanate XRD Data

2018 ◽  
pp. 05-11
Author(s):  
Bramadita A ◽  
Aji Hamim Wigena ◽  
Muhammad Nur Aidi
Keyword(s):  
Regression Model ◽  
Strontium Titanate ◽  
Parametric Method ◽  
Information Criterion ◽  
Ferroelectric Material ◽  
Spline Regression ◽  
Diffraction Intensity ◽  
Regression Curve ◽  
Fitting Method ◽  
High Flexibility

Characterization of ferroelectric material using X-Ray Diffraction tools (XRD) resulted in 2θ degree and intensity of diffraction data. The result of XRD characterization showed that the relationship between 2θ degree and diffraction intensity formed spectrum pattern; fluctuation of diffraction intensity value along with the increasing of 2θ degree. The high fluctuation of diffraction intensity value affected the inconsistency of mean and variance value. Thus the model used to estimate curve shape could not be analysed by parametric method which is strict with assumptions. Spline regression is a polynomial model with segmented character that has high flexibility to generate regression curve estimation through data fitting method. This research used the truncated spline regression model to estimate regression curve of Strontium Titanate (SrTiO3) and SrTiO3 doping with 2 % of RuO2 (SrTiO3+RuO2) XRD data. The best truncated spline regression model to estimate regression curve of SrTiO3 was model with 42 knots. This model has the smallest Akaike’s Information Criterion (AIC) value and the biggest R2, that is 13403.33 and 0.6503 respectively. Meanwhile, the best truncated spline regression model to estimate regression curve of SrTiO3 RuO2 was model with 37 knots. This model has the AIC value of 13953.68 and R2 of 0.6302.

scholarly journals Model Regresi Semiparametrik Spline Hasil Produksi Padi di Kabupaten Lombok Timur

2019 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Bidayani Bidayani ◽  
Mustika Hadijati ◽  
Nurul Fitriyani
Keyword(s):  
Regression Model ◽  
Sea Level ◽  
Cross Validation ◽  
Rice Production ◽  
Spline Regression ◽  
Determination Coefficient ◽  
Generalized Cross Validation ◽  
Selection Of

This study was conducted with the aim of determining the semiparametric spline regression model in the analysis of factors that influence rice production in East Lombok District in 2014 and finding out what factors influence the rice production results. The method used was semiparametric spline regression, with the selection of the optimum knot points using Generalized Cross Validation. The results obtained indicate that the variable that significantly affects rice production was the height of the area above sea level, with the determination coefficient value of 99.71% and the RMSEP value of 41.65.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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