scholarly journals Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

2021 ◽  
Vol 2123 (1) ◽  
pp. 012035
Author(s):  
Andi Tenri Ampa ◽  
I Nyoman Budiantara ◽  
Ismaini Zain
Keyword(s):  
Drinking Water ◽  
Fourier Series ◽  
Cross Validation ◽  
Semiparametric Regression ◽  
Gaussian Kernel ◽  
Regression Estimation ◽  
Mixed Estimator ◽  
Optimal Smoothing ◽  
Smoothing Parameters ◽  
Selection Of

Abstract In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.

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 Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

2017 ◽  
Vol 855 ◽  
pp. 012002 ◽  
Author(s):  
Ngizatul Afifah ◽  
I Nyoman Budiantara ◽  
I Nyoman Latra
Keyword(s):  
Fourier Series ◽  
Semiparametric Regression ◽  
Mixed Estimator

scholarly journals Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar

2021 ◽  
Vol 3 (1) ◽  
pp. 20
Author(s):  
Syafruddin Side ◽  
Wahidah Sanusi ◽  
Mustati'atul Waidah Maksum
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Cross Validation ◽  
Semiparametric Regression ◽  
Model Estimation ◽  
Mean Square ◽  
Semiparametric Regression Model ◽  
Regression Spline ◽  
Generalized Cross Validation ◽  
Selection Of

Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan  Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglobin (g/dL) dan umur (tahun), suhu tubuh ( ), trombosit ( ) sebagai komponen nonparametrik dengan nilai GCV minimum sebesar 221,67745153 dicapai pada titik knot yaitu 14,552; 14,987; dan 15,096; nilai MSE sebesar 199,1032; dan nilai koefisien determinasi sebesar 75,3% yang diperoleh dari model regresi semiparametrik spline linear dengan tiga titik knot..Kata Kunci: regresi semiparametrik, spline, knot, Generalized Cross Validation, Demam Berdarah Dengue.Abstract. Semiparametric regression is a regression model that includes parametric and nonparametric components in it. The regression model in this research is spline semiparametric regression with case studies of patients with Dengue Hemorrahagic Fever (DHF) at University of Hasanuddin Makassar Hospital during the period of January to March 2018. The best regression model estimation is obtained from the selection of optimal knot which has minimum Generalized Cross Validation (GCV) and Mean Square Error (MSE). Parametric component in this research is hemoglobin (g/dL) and age (years), body temperature ( ), platelets ( ) as a nonparametric components. The minimum value of GCV is 221,67745153 achieved at the point 14,552; 14,987; and 15,096 knot; MSE value of 199,1032; and the value of coefficient determination is 75,3% obtained from semiparametric regression model linear spline with third point of knots.Keywords: semiparametric regression, spline, knot, Generalized Cross Validation, Dengue Hemorrahagic Fever.

scholarly journals MODELING EAST JAVA INDONESIA LIFE EXPECTANCY USING SEMIPARAMETRIC REGRESSION MIXED SPLINE TRUNCATED AND FOURIER SERIES

2020 ◽  
Vol 13 (2) ◽  
pp. 149-160
Author(s):  
Khaerun Nisa' ◽  
I Nyoman Budiantara
Keyword(s):  
Fourier Series ◽  
Life Expectancy ◽  
Semiparametric Regression ◽  
Well Being ◽  
Predictor Variables ◽  
High Life Expectancy ◽  
A Value ◽  
Mixed Estimator ◽  
Different Characteristics

Life expectancy is one of the indicators used to evaluate the government’s performance in improving the well-being of the population. High life expectancy in an area indicates that people in the area have been assured of health and poverty has been well overcome, and vice versa. Based on national socioeconomic survey (SUSENAS) data, showing life expectancy in East Java Province from 2009 to 2013 increased by 69.15 years to 70,19 years. Although overall life expectancy in East Java province has increased, there are still some areas that have life expectancy below 65 years. This is not from the different characteristics of each religion. Therefore, the main purpose of this study is to model life expectancy in East Java using semiparametric regression with a mixed estimator of Spline Truncated and Fourier Series.  Based on the research that has been done, the results that modeling the data of life expectancy using mixed estimator of Spline Truncated and Fourier Series produced a value of R2 of 99,62% which means that the predictor variables are able to explain the response variabel life expectancy of 99.62%.

scholarly journals The Application of Mixed Smoothing Spline and Fourier Series Model in Nonparametric Regression

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2094
Author(s):  
Ni Putu Ayu Mirah Mariati ◽  
I. Nyoman Budiantara ◽  
Vita Ratnasari
Keyword(s):  
Fourier Series ◽  
Nonparametric Regression ◽  
Simulation Study ◽  
Random Error ◽  
Cross Validation ◽  
Daily Life ◽  
Mixed Data ◽  
Smoothing Spline ◽  
Poor Households ◽  
Mixed Estimator

In daily life, mixed data patterns are often found, namely, those that change at a certain sub-interval or that follow a repeating pattern in a certain trend. To handle this kind of data, a mixed estimator of a Smoothing Spline and a Fourier Series has been developed. This paper describes a simulation study of the estimator in nonparametric regression and its implementation in the case of poor households. The minimum Generalized Cross Validation (GCV) was used in order to select the best model. The simulation study used generation data with a Uniform distribution and a random error with a symmetrical Normal distribution. The result of the simulation study shows that the larger the sample size n, the better the mixed estimator as a model of nonparametric regression for all variances. The smaller the variance, the better the model for all combinations of samples n. Very poor households are characterized predominantly in their consumption of carbohydrates compared to that of fat and protein. The results of this study suggest that the distribution of assistance to poor households is not the same, because in certain groups there are poor households that consume higher carbohydrates, and some households may consume higher fats.

scholarly journals APLIKASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED (Studi Kasus: Pasien Demam Berdarah Dengue (DBD) di Rumah Sakit Puri Raharja)

2017 ◽  
Vol 6 (1) ◽  
pp. 65
Author(s):  
NI WAYAN MERRY NIRMALA YANI ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Body Temperature ◽  
Regression Model ◽  
Case Studies ◽  
Cross Validation ◽  
Single Point ◽  
Semiparametric Regression ◽  
Hemorrhagic Fever ◽  
Model Estimation ◽  
Semiparametric Regression Model ◽  
Selection Of

Semiparametric regression is a regression model that includes parametric components and nonparametric components in a model. The regression model in this research is truncated spline semiparametric regression with case studies of patients with Dengue Hemorrhagic Fever (DHF) at Puri Raharja Hospital during the period of January to March 2015. The best regression model estimation is obtained from the selection of optimal knots which has minimum Generalized Cross Validation (GCV) is. Parametric components in this research include age (years), body temperature (0C), platelets and hematocrit (%) as a nonparametric component. The minimum value of GCV is 0.03552045 achieved at the point of 39.6 knots, MSE value of 0.0296922; and the value of coefficient determination is 98.91%, obtained from semiparametric regression model truncated linear spline (order 2) with a single point of knots.

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