scholarly journals KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS

2014 ◽  
Vol 3 (4) ◽  
pp. 146
Author(s):  
HANY DEVITA ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Linear Correlation ◽  
Ridge Regression ◽  
Minimum Variance ◽  
Sum Of Squares ◽  
Least Square ◽  
Bias Parameter ◽  
Ridge Estimator ◽  
Ordinary Least Square ◽  
Parameter Estimations

Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity.  Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can  reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.

scholarly journals PENERAPAN METODE GENERALIZED RIDGE REGRESSION DALAM MENGATASI MASALAH MULTIKOLINEARITAS

2013 ◽  
Vol 2 (1) ◽  
pp. 54
Author(s):  
NI KETUT TRI UTAMI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Ridge Regression ◽  
Estimation Method ◽  
Minimum Variance ◽  
Least Square ◽  
Ordinary Least Square ◽  
Parameter Estimation Method ◽  
Alternative Method ◽  
Highly Correlated

Ordinary least square is parameter estimation method for linier regression analysis by minimizing residual sum of square. In the presence of multicollinearity, estimators which are unbiased and have a minimum variance can not be generated. Multicollinearity refers to a situation where regressor variables are highly correlated. Generalized Ridge Regression is an alternative method to deal with multicollinearity problem. In Generalized Ridge Regression, different biasing parameters for each regressor variables were added to the least square equation after transform the data to the space of orthogonal regressors. The analysis showed that Generalized Ridge Regression was satisfactory to overcome multicollinearity.

scholarly journals Employing particle swarm optimization algorithm for shrinkage parameter estimation in generalized Liu estimator

2020 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Qamar Abdulkareem Abdulazeez ◽  
Zakariya Yahya Algamal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Least Square ◽  
Superior Performance ◽  
Ridge Estimator ◽  
Swarm Optimization ◽  
Liu Estimator ◽  
Ordinary Least Square ◽  
Parameter Matrix

It is well-known that in the presence of multicollinearity, the Liu estimator is an alternative to the ordinary least square (OLS) estimator and the ridge estimator. Generalized Liu estimator (GLE) is a generalization of the Liu estimator. However, the efficiency of GLE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GLE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.   

scholarly journals Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Seyab Yasin ◽  
Sultan Salem ◽  
Hamdi Ayed ◽  
Shahid Kamal ◽  
Muhammad Suhail ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Mean Squared Error ◽  
Least Square ◽  
Ridge Estimator ◽  
Least Square Estimator ◽  
Squared Error ◽  
Joint Problem ◽  
Ridge Regression Estimator ◽  
Simulation Results ◽  
Two Parameter

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

Estimate The Parameters in Presence of Multicollinearity And Outliers Using Bisquare Weighted Ridge Least Median Squares Regression (wrlms)

2020 ◽  
Vol 23 (2) ◽  
pp. 9-24
Author(s):  
Kafi Dano Pati
Keyword(s):  
Simulation Study ◽  
Ridge Regression ◽  
Robust Regression ◽  
Mean Squared Error ◽  
Medical Application ◽  
Least Square ◽  
Potential Candidate ◽  
Robust Detection ◽  
Ordinary Least Square ◽  
Numerical Example

The presence of multicollinearity and outliers are classical problems of data within the linear regression framework. We are going to present a proposal of a new method which can be a potential candidate for robust ridge regression as well as a robust detection of multicollinearity. This proposal arises as a logical combination of principles used in the ridge regression and the Bisquare weighted function. The technique of the Least Median of Squares (LMS) is used for the sake of overcoming the resulting regression problems. This paper investigates the non-resistance of Ordinary Least Square (OLS) to multicollinearity and outliers and proposes the utilization of robust regression for instance, Least Median Squares LMS to detect non-normality of residuals, the use of robust methods yields more reliable trend estimations and outlier detection. LMS is introduced as a robust regression technique and through medical application its effect on regression is discussed. The numerical example and simulation study shows that the outcome of the Weighted Ridge Least Median Squares (WRLMS) is better than other estimators in terms of its efficiency. This has been done by utilizing both Standard Error (SE) and the Root Mean Squared Error criterion for the numerical example and simulation study, respectively as far as a lot of combinations of error distribution and degree of multicollinearity are concerned.

REGULARIZATION METHODS FOR ESTIMATING A MULTI-FACTOR CORPORATE BOND PRICING MODEL: AN APPLICATION FOR BRAZIL

2021 ◽  
pp. 2150005
Author(s):  
PAULO ROBERTO GUIMARÃES ◽  
OSVALDO CANDIDO ◽  
ANDRÉ RONZANI
Keyword(s):  
Mean Squared Error ◽  
Predictive Performance ◽  
Least Square ◽  
Corporate Bond ◽  
Ridge Estimator ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Optimal Value ◽  
Selection Operator ◽  
Public Offering

The present work focused on studying which factors affect Brazilian inflation-linked corporate bond prices in a primary market setting. The explanatory variables tested were rating, maturity, duration, issuer governance level, industrial classification, collateral, tax exemption, public offering modality, financial volume, coupon frequency, number of issues, number of days since going public, and the Brazilian basic interest rate target. In order to choose the set of variables with best predictive performance, best subsets ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO) were applied on a testing sample. For estimating purposes, we also tested the Ridge estimator. For both LASSO and Ridge, we used the k-fold approach to choose the optimal value for the lambda penalty. In terms of smallest mean squared error, the OLS estimator outperformed both the Ridge and the LASSO. This result suggests that the variance-bias trade-off might not be a concern for the Brazilian case.

scholarly journals Modelling Tap Water Consumer Ratio

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1557
Author(s):  
Meltem Ekiz ◽  
Osman Ufuk Ekiz
Keyword(s):  
Tap Water ◽  
Least Square ◽  
Short Term ◽  
Third Order ◽  
Ordinary Least Square ◽  
Water Policies ◽  
Parameter Estimations ◽  
Air Temperatures ◽  
M Estimator ◽  
Curve Model

Increasing population and the rising air temperatures are known as factors that cause water depletion in the watersheds. Therefore, it is important to accurately predict the future ratios of tap water consumers using the same watershed to the population living in the specified area, to produce better water policies and to take the necessary measures. Predictions can be made by a growth curve model (GCM). Parameter estimations of the GCM are usually based on the ordinary least square (OLS) estimator. However, the outlier presence affects the estimations and the predictions, which are obtained by using the estimated model. The present article attempts to construct first- and third-order GCMs with robust least median square (LMS) and M estimators to make short-term predictions of ratios of tap water consumers. According to the findings, parameter estimations of the models, the outliers, and the predictions vary with respect to the estimators. The M estimator for short-term predictions is suggested for use, due to its robustness against outlier points.

scholarly journals التعدد الخطي في الأنحدار المتعدد اللامعلمي , الكشف و المعالجة بأستعمال المحاكاة

2017 ◽  
Vol 23 (101) ◽  
pp. 495
Author(s):  
لقاء علي محمد ◽  
صابرين حسين كاظم
Keyword(s):  
Nonparametric Regression ◽  
Ridge Regression ◽  
Least Square Method ◽  
Least Square ◽  
Kernel Ridge Regression ◽  
Ordinary Least Square ◽  
Rule Of Thumb

المستخلص               يعتبر تحليل الأنحدار هو الحجر الأساس لعلم الأحصاء , و الذي يعتمد في الغالب على طريقة المربعات الصغرى الأعتيادية Ordinary Least Square Method , لكن كما هو معروف ان الطريقة المذكورة انفآ لها عدة شروط  كي تعمل بدقة و بنتائج يمكن الأعتماد عليها , اضافة الى إن عدم توفر بعض من شروطها يجعل من المستحيل اتمام العمل و تحليل النماذج و من ضمن تلك الشروط هي عدم وجود مشكلة التعدد الخطي ( Multi-CoLinearity ) و نحن في صدد الكشف عن وجود تلك المشكلة بين المتغيرات التوضيحية بأستعمال اختبار فيرار كلوبر, بالأضافة الى شرط خطية البيانات و لعدم توفر الشرط الأخير تم اللجوء الى الأنحدار اللامعلمي (Nonparametric Regression ) و معالجة المشكلة بإستعمال دالة انحدار الحرف اللبي Kernel Ridge Regression و التي تعتمد على تقدير عرض الحزمة (معلمة التمهيد) و لذلك تم اللجوء الى طريقتين مختلفتين لتقدير المعلمة التمهيدية و هما طريقة قاعدة الأبهام Rule of thumb ( RULE) و الطريقة التمهيدية Bootstrap (BOOT) و المقارنة بين تلك الطرق بأستعمال اسلوب المحاكاة .

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

PENGARUH PENANAMAN MODAL ASING, PERDAGANGAN INTERNASIONAL, GDP PER KAPITA, SEKTOR PERTANIAN, DAN URBANISASI TERHADAP PENDUDUK BEKERJA DI INDONESIA

2017 ◽  
Vol 21 (2) ◽  
pp. 85-95
Author(s):  
John Marcell Rumondor
Keyword(s):  
International Trade ◽  
Gross Domestic Product ◽  
Foreign Investment ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Domestic Product ◽  
Working Population ◽  
Ordinary Least Square ◽  
Per Capita

This research aims to understand the influenceof foreign investment, international trade, Gross Domestic Product per capita, agriculture and urbanization of the working population. Country used as an object in this research is Indonesia. This research uses the method of analysis Ordinary Least Square (OLS) and the multiple linear regression analysis method. Research period are from 1997 – 2012. The results showed that the international trade, Gross Domestic Product per capita, agriculture and urbanization have significantpositive influenceon the population work in Indonesia, but foreign investment has no significanteffect on the working population in Indonesia.

PENGARUH KNOWLEDGE MANAGEMENT, KOMPETENSI, MOTIVASI TERHADAP KINERJA KARYAWAN

2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin
Keyword(s):  
Knowledge Management ◽  
Linear Regression ◽  
Significant Influence ◽  
Employee Performance ◽  
Least Square Method ◽  
Least Square ◽  
Analysis Tool ◽  
Ordinary Least Square ◽  
Competence Motivation ◽  
Performance Analyze

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.

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