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 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.

scholarly journals PEMODELAN REGRESI NONPARAMETRIK SPLINE TRUNCATED PADA FAKTOR-FAKTOR YANG MEMPENGARUHI PERTUMBUHAN EKONOMI DI PROVINSI MALUKU

2019 ◽  
Vol 1 (1) ◽  
pp. 27-37
Author(s):  
Muhammad Yahya Matdoan ◽  
A. M. Balami ◽  
M. W. Talakua
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Cross Validation ◽  
Regression Method ◽  
Behavior Patterns ◽  
Spline Regression ◽  
Model Data ◽  
Truncated Regression ◽  
A Value ◽  
Poor Population

Economic growth is a benchmark for the success of a region's development, especially in the economic field. The purpose of economic development in an area is basically to improve the welfare and prosperity of the community. Economic growth in Maluku Province experienced a positive increase. However, there is still a disparity between districts/cities in Maluku Province, which has an impact on increasing unemployment and an increasingly poor population. This is inseparable from the influencing factors so that it can be precisely done by modeling these factors using the truncated nonparametric spline regression method. the advantages of this method occur because in nonparametric spline truncated regression has knot points, which are joint fusion points that indicate changes in data behavior patterns. Besides, this method can be used to model data patterns that change at certain sub-intervals. The best model is very dependent on determining the optimal knot point by using the minimum Generalized Cross-Validation (GCV) value. The results obtained in this study were the highest percentage of economic growth in Maluku Province, Ambon City with a percentage of 6.17%, and the lowest economic growth was in the East Seram District (SBT) with a percentage of 5.03%. Furthermore, the best model is obtained with a model with three knots and a GCV value of 11.61, a value of 2 of 0.94 and an MSE value of 0.005. This means that statistically, the variables used in this study affect economic growth by 94%. While the rest is influenced by other variables outside the research.

scholarly journals Analisis Pengaruh Tingkat Bunga, Tingkat Inflasi, Earning Per Share Terhadap Harga Saham PT. Bank Mandiri (persero) Tbk.

2018 ◽  
Vol 3 (1) ◽  
pp. 011
Author(s):  
Didi Rahmat
Keyword(s):  
Regression Analysis ◽  
Interest Rate ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Stock Price ◽  
Inflation Rate ◽  
Analysis Method ◽  
Independent Variable

In this research, we investigate impact of Inflation Rate, Interes Rate and Earning per Share as independent variable to the PT. Bank Mandiri (persero) Tbk. stock price. We use causality research with multiple regression analysis method to answer the hypothesis. As conclusion that find, all independent variabel have impact significantly to the stock price in 0.78%. In partial, inflation has no impact to the stock price. In other way, interest rate have significantly influence to the stock price. But the influence is negative. EPS fositive have influence significantly to stock price.

scholarly journals A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data

Stats ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 120-136
Author(s):  
Ersin Yılmaz ◽  
Syed Ejaz Ahmed ◽  
Dursun Aydın
Keyword(s):  
Nonparametric Regression ◽  
Censored Data ◽  
Kernel Smoothing ◽  
Synthetic Data ◽  
Regression Function ◽  
Data Transformation ◽  
Smoothing Splines ◽  
Spline Regression ◽  
Real World Data ◽  
Right Censored Data

This paper aims to solve the problem of fitting a nonparametric regression function with right-censored data. In general, issues of censorship in the response variable are solved by synthetic data transformation based on the Kaplan–Meier estimator in the literature. In the context of synthetic data, there have been different studies on the estimation of right-censored nonparametric regression models based on smoothing splines, regression splines, kernel smoothing, local polynomials, and so on. It should be emphasized that synthetic data transformation manipulates the observations because it assigns zero values to censored data points and increases the size of the observations. Thus, an irregularly distributed dataset is obtained. We claim that adaptive spline (A-spline) regression has the potential to deal with this irregular dataset more easily than the smoothing techniques mentioned here, due to the freedom to determine the degree of the spline, as well as the number and location of the knots. The theoretical properties of A-splines with synthetic data are detailed in this paper. Additionally, we support our claim with numerical studies, including a simulation study and a real-world data example.

Perimeter-based Fourier analysis: a new morphometric method applied to the trilobite cranidium

1989 ◽  
Vol 63 (6) ◽  
pp. 880-885 ◽  
Author(s):  
Mike Foote
Keyword(s):  
Fourier Analysis ◽  
Fourier Method ◽  
Radial Distance ◽  
Closed Curve ◽  
Shape Information ◽  
Morphometric Method ◽  
Higher Taxa ◽  
Starting Point ◽  
Dependent Variables ◽  
Independent Variable

A new Fourier method is presented to quantify shapes too complex to be described by conventional polar Fourier analysis. The length along a closed curve serves as the independent variable. The centroid of the curve is determined and for each point on the curve two different dependent variables are defined, based on: 1) the angle defined by the starting point, the centroid, and the point on the curve; and 2) the radial distance from the centroid to the point on the curve. The method is used to describe the trilobite cranidium, and 12 harmonic coefficients are found to summarize 99 percent of the shape information contained in the cranidial outline. In an application to trilobite evolution during the Cambrian and Ordovician, it is found that higher taxa of trilobites become progressively more distinct morphologically. This result is in agreement with previous qualitative observations, and is attributable to an increase in morphologic dispersion among higher taxa, but not to a decrease in morphologic dispersion within higher taxa.

scholarly journals The Estimation of Residual Variance in Nonparametric Regression

2021 ◽  
Vol 17 (3) ◽  
pp. 438-446
Author(s):  
Abdul Wahab ◽  
I Nyoman Budiantara ◽  
Kartika Fitriasari
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Unknown Function ◽  
Residual Variance ◽  
Variance Estimator ◽  
Nonparametric Regression Model ◽  
Data Simulation ◽  
Independent Variable ◽  
Variable G

Given a nonparametric regression model Yi = g(xi) + ei,    i = 1, 2, …, n, where Y is a dependent variable, x is an independent variable, g is an unknown function and e is an error assumed to be an independent, identical, and is distributed with mean 0 and variance σ2. In this research Rice estimator is used to determine the biased value of a residual variance estimator. The Rice estimator is given as follows: . The biased value of residual variance estimator of the Rice method is: , where  and. Using the Rice estimator, the Tong-Wang residual variance estimator is obtained, that is: , Where   , , , , ,  k = 1, 2, … , m. Based upon the data simulation by considering the exponential, arithmetical, and trigonometrical models, it is found that the MSE value of the Tong-Wang estimator tends to be less compared to those of the Rice estimator as well as the GSJ (Gasser, Sroka, and Jennen) estimator.

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

scholarly journals PENGARUH LAJU PERTUMBUHAN INFLASI, HARGA MINYAK DUNIA DAN DOW JONES INDUSTRIAL AVERAGE TERHADAP INDEKS HARGA SAHAM GABUNGAN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2007-2011

2018 ◽  
Vol 2 (2) ◽  
pp. 176
Author(s):  
Rully Putra Surya Pratama ◽  
Indah Kurniawati
Keyword(s):  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Stock Exchange ◽  
Oil Price ◽  
Independent Variables ◽  
Analysis Technique ◽  
Dow Jones Industrial Average ◽  
Independent Variable ◽  
Stock Price Index

This research attempts to influence the inflation growth, the oil price dow jones industrial average to Composite Stock Price Index which is registred in indonesian stock exchange in the period of 2007-2011. The population in this research was the whole index in indonesian stock exchange (ISE). The sample was taken based on sampling purpodive. The data analysis technique used double linier regression. The dependent variable in this research was the growth of Composite Stock Price Index, while the independent variable was the inflation growth, the oil price and dow jones industrial average. The results of this research showed that the almost dependent variable used had effects for the growth of Composite Stock Price Index. The inflation rate growth variable did not have effects on Composite Stock Price Index growth, while the oil price rate variable and dow jones industrial average had effects on Composite Stock Price Index growth. Simultaneously, the independent variables examinedhere had effects on Composite Stock Price Index growth.

Sign in / Sign up

Export Citation Format

Share Document