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 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 Spline-Fourier’s Method for Modelling Inflation in Indonesia

2018 ◽  
Vol 73 ◽  
pp. 13003
Author(s):  
Suparti Suparti ◽  
Alan Prahutama ◽  
Rukun Santoso ◽  
Alvita Rachma Devi
Keyword(s):  
Nonparametric Regression ◽  
Inflation Rate ◽  
Cross Validation ◽  
Fourier Method ◽  
Regression Method ◽  
Spline Regression ◽  
Shape Modelling ◽  
Fourier Regression ◽  
Independent Variable ◽  
Fourier Model

Regression method is a statistical method for modelling dependent variable with independent variable. Nonparametric regression is an approach to regression analysis that is suitable for data that have an unknown curve shape. Modelling by using nonparametric regression method does not require any assumptions. Spline and Fourier methods are some of the estimators in nonparametric regression. The spline method requires optimal knots to obtain the best model. The most commonly used method to determine the optimal knots is Generalized Cross Validation (GCV). The Fourier method is a method based on the cosine and sinus series. The Fourier method is particularly suitable for data that experience repetitive patterns. This study modeled the Inflation rate in Indonesia from January 2007 to August 2017. The dependent variable is inflation rate, while the independent variable is time. From the result, linear spline regression estimation with three knots that generates R square of 60%. The best Fourier model is Fourier with K = 100 that generates R square of 80.12%. The best Spline model is with 9 knots generates R square of 87.65%, so, for inflation modelling in Indonesia, the spline regression model generates a simpler model with better R-square than Fourier regression.

scholarly journals Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar)

2020 ◽  
Vol 2 (1) ◽  
pp. 70
Author(s):  
Wahidah Sanusi ◽  
Rahmat Syam ◽  
Rabiatul Adawiyah
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Nonparametric Regression ◽  
Gestational Age ◽  
Cross Validation ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Generalized Cross Validation ◽  
Child Hospital ◽  
The Relationship

Pendekatan nonparametrik merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel prediktornya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Spline merupakan suatu teknik yang dilakukan untuk mengestimasi parameter dalam regresi nonparametrik. Penelitian ini bertujuan untuk mengetahui model hubungan antara berat badan lahir rendah dan faktor-faktor yang mempengaruhi berdasarkan model spline. Faktor-faktor tersebut adalah usia ibu, usia kehamilan, dan jarak kehamilan. Data tersebut diperoleh dari rumah sakit ibu dan anak siti Fatimah Makassar tahun 2017. Dimana untuk mendapatkan model spline terbaik langkah awal yang dilakukan adalah menentukan knot dengan nilai Generalized Cross Validation (GCV) yang minimum. Berdasarkan penelitian yang telah dilakukan, dua variabel dinyatakan berpengaruh terhadap berat badan lahir rendah yaitu usia ibu, dan usia kehamilan. Model regresi nonparametrik dengan pendekatan Spline yang terbentuk memiliki koefisien determinasi sebesar 78,19%, serta nilai GCV dengan tiga titik knot yaitu 0.0117.Kata kunci: Regresi Nonparametrik, Spline, Berat Badan Lahir Rendah, Generalized Cross Validation The non-parametric approach is an approach that is used if the form of the relationship between the response variable and the predictor variable is unknown or the absence of information about the shapes of regression functions. The Spline is a technique performed to estimate the parameters in the nonparametric regression. This study aims to determine the model of the relationship between low birth weight and the factors that affect the based on the spline model. Such factors are maternal age, gestational age, and pregnancy distance. The Data is obtained from the mother and child hospital siti Fatimah Makassar 2017. Where to get a spline model best the initial step is to determine the knots with the value of the Generalized Cross Validation (GCV) which is a minimum. Based on the research that has been done, the two variables stated effect against low birth weight, namely age of mother, and gestational age. Nonparametric regression Model with the approach of the Spline that is formed has a coefficient of determination of 78.19 to%, as well as the value of the GCV with a three-point knot that is 0.0117.Keyword : Nonparametric Regression, Spline, Low Birth Weight, Generalized Cross Validation

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