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. 

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 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

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.

scholarly journals COMPARING THE PREDICTIVE PERFORMANCE OF OLS AND 7 ROBUST LINEAR REGRESSION ESTIMATORS ON A REAL AND SIMULATED DATASETS

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Sacha Varin
Keyword(s):  
Linear Regression ◽  
High Efficiency ◽  
Robust Regression ◽  
Mean Squared Error ◽  
Predictive Performance ◽  
Ordinary Least Squares ◽  
Small Samples ◽  
Robust Estimators ◽  
Absolute Deviation ◽  
M Estimator

Robust regression techniques are relevant tools for investigating data contaminated with influential observations. The article briefly reviews and describes 7 robust estimators for linear regression, including popular ones (Huber M, Tukey’s bisquare M, least absolute deviation also called L1 or median regression), some that combine high breakdown and high efficiency [fast MM (Modified M-estimator), fast ?-estimator and HBR (High breakdown rank-based)], and one to handle small samples (Distance-constrained maximum likelihood (DCML)). We include the fast MM and fast ?-estimators because we use the fast-robust bootstrap (FRB) for MM and ?-estimators. Our objective is to compare the predictive performance on a real data application using OLS (Ordinary least squares) and to propose alternatives by using 7 different robust estimations. We also run simulations under various combinations of 4 factors: sample sizes, percentage of outliers, percentage of leverage and number of covariates. The predictive performance is evaluated by crossvalidation and minimizing the mean squared error (MSE). We use the R language for data analysis. In the real dataset OLS provides the best prediction. DCML and popular robust estimators give good predictive results as well, especially the Huber M-estimator. In simulations involving 3 predictors and n=50, the results clearly favor fast MM, fast ?-estimator and HBR whatever the proportion of outliers. DCML and Tukey M are also good estimators when n=50, especially when the percentage of outliers is small (5% and 10%%). With 10 predictors, however, HBR, fast MM, fast ? and especially DCML give better results for n=50. HBR, fast MM and DCML provide better results for n=500. For n=5000 all the robust estimators give the same results independently of the percentage of outliers. If we vary the percentages of outliers and leverage points simultaneously, DCML, fast MM and HBR are good estimators for n=50 and p=3. For n=500, fast MM, fast ? and HBR provi

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.

scholarly journals Response Surface Model with Comparison of OLS Estimation and MM Estimation

2021 ◽  
Vol 5 (2) ◽  
pp. 273-283
Author(s):  
Salsabila Basalamah ◽  
Edy Widodo
Keyword(s):  
Response Surface ◽  
Robust Regression ◽  
Estimation Method ◽  
Least Square ◽  
Surface Model ◽  
Method Of Moment ◽  
Optimal Point ◽  
Rsm Model ◽  
Mm Estimation

Response Surface Method (RSM) is a collection of statistical techniques in the form of experiments and regression, as well as mathematics that is useful for developing, improving, and optimizing processes. In general, the determination of models in RSM is estimated by linear regression with Ordinary Least Square (OLS) estimation. However, OLS estimation is very weak in the presence of data identified as outliers, so in determining the RSM model a strong and resistant estimation is needed namely robust regression. One estimation method in robust regression is the Method of Moment (MM) estimation. This study aims to compare the OLS estimation and MM estimation method to get the optimal point of response in this case study. Comparison of the best estimation models using the parameters MSE and R^2 adj. The results of MM estimation give better results to the optimal response results in this case study.

scholarly journals PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL

2021 ◽  
Vol 10 (1) ◽  
pp. 44-54
Author(s):  
Maghfiroh Hadadiah Mukrom ◽  
Hasbi Yasin ◽  
Arief Rachman Hakim
Keyword(s):  
Life Expectancy ◽  
Regression Model ◽  
Spatial Regression ◽  
Spatial Effects ◽  
Regression Parameter ◽  
Spatial Durbin Model ◽  
Spatial Regression Model ◽  
Central Java ◽  
Regression Parameters ◽  
M Estimator

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.

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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