scholarly journals REGRESI ROBUST ESTIMASI-M DENGAN PEMBOBOT ANDREW, PEMBOBOT RAMSAY DAN PEMBOBOT WELSCH MENGGUNAKAN SOFTWARE R

2019 ◽  
Vol 8 (3) ◽  
pp. 377-388
Author(s):  
Aulia Desy Deria ◽  
Abdul Hoyyi ◽  
Mustafid Mustafid
Keyword(s):  
Parameter Estimation ◽  
Weight Function ◽  
Robust Regression ◽  
Participation Rate ◽  
Ordinary Least Squares ◽  
Regression Methods ◽  
Central Java ◽  
School Participation ◽  
M Estimation

Robust regression is one of the regression methods that robust from effect of outliers. For the regression with the parameter estimation used Ordinary Least Squares (OLS), outliers can caused assumption violation, so the estimator obtained became bias and inefficient. As a solution, robust regression M-estimation with Andrew, Ramsay and Welsch weight function can be used to overcome the presence of outliers. The aim of this study was to develop a model for case study of poverty in Central Java 2017 influenced by the number of unemployment, population, school participation rate, Human Development Index (HDI), and inflation. The result of estimation using OLS show that there is violation of heteroskedasticity caused by the presence outliers. Applied robust regression to case study proves robust regression can solve outliers and improve parameter estimation. The best robust regression model is robust regression M-estimation with Andrew weight function. The influence value of predictor variables to poverty is 92,7714% and MSE value is 370,8817. Keywords: Outliers, Robust Regression, M-Estimator, Andrew, Ramsay, Welsch

scholarly journals PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 24-34
Author(s):  
Eka Destiyani ◽  
Rita Rahmawati ◽  
Suparti Suparti
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Predictor Variables ◽  
Healthy Life ◽  
Significance Level ◽  
Central Java ◽  
Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM  Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords:  Ordinary  Least  Squares  (OLS),  Multicollinearity,  Outliers,  RidgeRegression, Robust Regression, AKB.

scholarly journals PEMODELAN REGRESI RIDGE ROBUST S,M, MM-ESTIMATOR DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah Tahun 2020)

2021 ◽  
Vol 10 (3) ◽  
pp. 402-412
Author(s):  
Anggun Perdana Aji Pangesti ◽  
Sugito Sugito ◽  
Hasbi Yasin
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Method Of Moment ◽  
Rate Dependency ◽  
Central Java ◽  
Regression Parameters ◽  
M Estimator ◽  
Violation Of Assumptions ◽  
Method Of Moment Estimator

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linier regression parameters. If there is a violation of assumptions such as multicolliniearity especially coupled with the outliers, then the regression with OLS is no longer used. One method can be used to solved the multicollinearity and outliers problem is Ridge Robust Regression.  Ridge Robust Regression is a modification of ridge regression method used to solve the multicolliniearity and using some estimators of robust regression used to solve the outlier, the estimator including : Maximum likelihood estimator (M-estimator), Scale estimator (S-estimator), and Method of moment estimator (MM-estimator). The case study can be used with this method is data with multicollinearity and outlier, the case study in this research is poverty in Central Java 2020 influenced by life expentancy, unemployment number, GRDP rate, dependency ratio, human development index, the precentage of population over 15 years of age with the highest education in primary school, mean years school. The result of estimation using OLS show that there is a multicollinearity and presence an outliers. Applied the ridge robust regression to case study prove that ridge robust regression can improve parameter estimation. The best ridge robust regression model is Ridge Robust Regression S-Estimator. The influence value of predictor variabels to poverty is 73,08% and the MSE value is 0,00791. 

Robust Weighted Least Squares Estimation of Regression Parameter in the Presence of Outliers and Heteroscedastic Errors

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Bello Abdulkadir Rasheed ◽  
Robiah Adnan ◽  
Seyed Ehsan Saffari ◽  
Kafi Dano Pati
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Model Parameters ◽  
Constant Variance ◽  
Regression Parameters ◽  
M Estimation

In a linear regression model, the ordinary least squares (OLS) method is considered the best method to estimate the regression parameters if the assumptions are met. However, if the data does not satisfy the underlying assumptions, the results will be misleading. The violation for the assumption of constant variance in the least squares regression is caused by the presence of outliers and heteroscedasticity in the data. This assumption of constant variance (homoscedasticity) is very important in linear regression in which the least squares estimators enjoy the property of minimum variance. Therefor e robust regression method is required to handle the problem of outlier in the data. However, this research will use the weighted least square techniques to estimate the parameter of regression coefficients when the assumption of error variance is violated in the data. Estimation of WLS is the same as carrying out the OLS in a transformed variables procedure. The WLS can easily be affected by outliers. To remedy this, We have suggested a strong technique for the estimation of regression parameters in the existence of heteroscedasticity and outliers. Here we apply the robust regression of M-estimation using iterative reweighted least squares (IRWLS) of Huber and Tukey Bisquare function and resistance regression estimator of least trimmed squares to estimating the model parameters of state-wide crime of united states in 1993. The outcomes from the study indicate the estimators obtained from the M-estimation techniques and the least trimmed method are more effective compared with those obtained from the OLS.

Parameter Estimation in Marketing Models in the Presence of Influential Response Data: Robust Regression and Applications

1984 ◽  
Vol 21 (3) ◽  
pp. 268-277 ◽  
Author(s):  
Vijay Mahajan ◽  
Subhash Sharma ◽  
Yoram Wind
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Parameter Estimates ◽  
Response Data ◽  
Marketing Models ◽  
Marketing Data

In marketing models, the presence of aberrant response values or outliers in data can distort the parameter estimates or regression coefficients obtained by means of ordinary least squares. The authors demonstrate the potential usefulness of the robust regression analysis in treating influential response values in marketing data.

scholarly journals ANALISIS EFISIENSI DAN FAKTOR-FAKTOR PRODUKSI KOMODITAS SEKTOR BASIS KABUPATEN PATI (Studi Kasus Budidaya Ikan Bandeng Kabupaten Pati, Jawa Tengah)

2019 ◽  
Vol 14 (1) ◽  
pp. 59
Author(s):  
Triana Dwi Wahyuni ◽  
Sasongko Sasongko ◽  
Sri Muljaningsih
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Large Scale ◽  
Ordinary Least Squares ◽  
Data Envelopment ◽  
Central Java ◽  
Factors Analysis ◽  
Production Factors ◽  
Factors Influencing

Penelitian ini bertujuan untuk mengukur tingkat efisiensi teknik pada pembudidaya ikan bandeng dan faktor-faktor yang mempengaruhi produksi ikan bandeng sebagai komoditas sektor basis di Kabupaten Pati. Metode penelitian yang digunakan adalah dengan analisis DEA (Data Envelopment Analysis) dengan asumsi output oriented dan pendekatan Variable Return to Scale (VRS) untuk mengukur tingkat efisiensi teknik pembudidaya bandeng. Selanjutnya dengan analisis regresi linear berganda, untuk mengetahui faktor-faktor yang mempengaruhi produksi bandeng di Kabupaten Pati. Hasil penelitian menunjukkan bahwa tingkat efisiensi teknis pembudidaya bandeng di Kabupaten Pati masih sangat rendah, rata-rata efisiensi teknis adalah 7,41. Adapun sebanyak 55% atau sebanyak 44 pembudidaya dari 80 sampel pembudidaya masih berada di bawah rata-rata. Hasil analisis regresi diperoleh bahwa penggunaan benih, luas lahan, dan jarak lokasi tambak dengan laut mempunyai pengaruh yang sangat signifikan; Sedangkan penggunaan tenaga kerja tidak berpengaruh secara signifikan terhadap produksi bandeng.Efficiency and Production Factors Analysis of Base Sector  Commodity in the Pati Regency (Case Study: Milkfish Farming  in Pati Regency, Central Java)This study aims to measure the level of technical efficiency in milkfish farmers and factors influencing milkfish production as a base sector commodity in Pati Regency. The research applied DEA (Data Envelopment Analysis) with output oriented assumption and Variable Return to Scale (VRS) approaches to measure the efficiency level of milkfish farmers. It is then analysed by Ordinary Least Squares (OLS) to determine factors influencing milkfish production in Pati Regency. Results showed that the level of technical efficiency of milkfish farmers in Pati Regency was in low level with average number of 7.41. There are 55% of 80 farmers are below average. Furthermore, this research described the efficiency level of milkfish farmers in low, medium and large scale. OLS analysis found that the use of seeds, land area, and distance between ponds and sea have significant effect on milkfish production instead of the use of labour. 

scholarly journals PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah)

2019 ◽  
Vol 8 (1) ◽  
pp. 81-92
Author(s):  
Dhea Kurnia Mubyarjati ◽  
Abdul Hoyyi ◽  
Hasbi Yasin
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Predictor Variables ◽  
Fish Production ◽  
Significance Level ◽  
Central Java ◽  
Approximate Estimation

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

Performance of Robust Regression Methods in Real-Time Polymerase Chain Reaction Calibration

2014 ◽  
Vol 29 (4) ◽  
pp. 317-327 ◽  
Author(s):  
Annalisa Orenti ◽  
Ettore Marubini
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Robust Regression ◽  
Control Program ◽  
Ordinary Least Squares ◽  
Quality Control Program ◽  
Absolute Deviation ◽  
Nominal Level ◽  
Regression Methods ◽  
Polymerase Chain

The ordinary least squares (OLS) method is routinely used to estimate the unknown concentration of nucleic acids in a given solution by means of calibration. However, when outliers are present it could appear sensible to resort to robust regression methods. We analyzed data from an External Quality Control program concerning quantitative real-time PCR and we found that 24 laboratories out of 40 presented outliers, which occurred most frequently at the lowest concentrations. In this article we investigated and compared the performance of the OLS method, the least absolute deviation (LAD) method, and the biweight MM-estimator in real-time PCR calibration via a Monte Carlo simulation. Outliers were introduced by replacement contamination. When contamination was absent the coverages of OLS and MM-estimator intervals were acceptable and their widths small, whereas LAD intervals had acceptable coverages at the expense of higher widths. In the presence of contamination we observed a trade-off between width and coverage: the OLS performance got worse, the MM-estimator intervals widths remained short (but this was associated with a reduction in coverages), while LAD intervals widths were constantly larger with acceptable coverages at the nominal level.

THE CHANGING OF SANTRI’s PREFERENCE TOWARDS STUDIES AND PROFESSIONS: A Case Study at Pondok Pesantren Alhikmah 2 Brebes, Central Java

Al-Qalam ◽  
2017 ◽  
Vol 23 (2) ◽  
Author(s):  
Hayyadin Ode
Keyword(s):  
Subject Matter ◽  
Central Java ◽  
Religious Subject

<p>This research aimed to figure out the santri’s preference toward studies and professions in which conduct study at pesantren. Common perceived and stated also at Government Ordinancenumber 55, 2007, that pesantren purposes was to reproduce Islamic scholar (ulama). However, through this study, it proved that not all santri wanted to be ulama, most of them wanted to be a scientist. This study was a case study, conducted in 2015 at Pesantren Alhikmah2 Brebes. Data collected using questionnaire, interview, and document. Those all derived from santris, Kyais, and teachers (asatidz). The research concluded as showed from questionnaire that santri’s  preferences toward study has gotten  changing to general subject matters instead of religious subject matters; and the santri’s professions and jobspreference has gotten changing to the jobs and professions that based on general subject matter, instead of choose to be ulama (Islamic scholar) most of santri wanted to be scientists, or researchers, or doctors as well as athlete.</p>

scholarly journals Female Candidates, Islamic Women’s Organisations, and Clientelism in the 2019 Indonesian Elections

2021 ◽  
Vol 40 (1) ◽  
pp. 73-92
Author(s):  
Muhammad Mahsun ◽  
Misbah Zulfa Elizabeth ◽  
Solkhah Mufrikhah
Keyword(s):  
Structural Barriers ◽  
Women Candidates ◽  
Electoral Success ◽  
Female Candidates ◽  
Central Java ◽  
Grassroots Support ◽  
Nahdlatul Ulama ◽  
And Gender ◽  
Women's Networks

This article analyses the factors leading to the success of women candidates in the 2019 elections in Central Java. Recent scholarship on women’s representation in Indonesia has highlighted the role that dynastic ties and relationships with local political elites play in getting women elected in an environment increasingly dominated by money politics and clientelism. Our case study of women candidates in Central Java belonging to the elite of the Nahdlatul Ulama (NU)-affiliated women’s religious organisations Muslimat and Fatayat shows that strong women candidates with grassroots support can nonetheless win office. Using the concepts of social capital and gender issue ownership, and clientelism, we argue that women candidates can gain a strategic advantage when they “run as women.” By harnessing women’s networks and focusing on gender issues to target women voters, they are able to overcome cultural, institutional, and structural barriers to achieve electoral success even though they lack resources and political connections.

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