scholarly journals KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL

2020 ◽  
Vol 4 (1) ◽  
pp. 97-115
Author(s):  
Khusnul Khotimah ◽  
Kusman Sadik ◽  
Akbar Rizki
Keyword(s):  
Regression Analysis ◽  
Robust Regression ◽  
Regression Line ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Production Data ◽  
Estimation Bias ◽  
Data Simulation ◽  
Multi Stage ◽  
Line Equation

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.

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.

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.

Robust Regression Approach to Analyzing Fisheries Data

1994 ◽  
Vol 51 (6) ◽  
pp. 1420-1429 ◽  
Author(s):  
Y. Chen ◽  
D. A. Jackson ◽  
J. E. Paloheimo
Keyword(s):  
Regression Analysis ◽  
Robust Regression ◽  
Residual Analysis ◽  
Regression Method ◽  
Theoretical Justification ◽  
Independent Variables ◽  
Normality Assumption ◽  
Step Procedure ◽  
Regression Approach ◽  
Atypical Values

Fisheries data often contain inaccuracies due to various errors, if such errors meet the Gauss–Markov conditions and the normality assumption, strong theoretical justification can be made for traditional least-squares (LS) estimates. However, these assumptions are not always met. Rather, it is more common that errors do not follow the Gauss–Markov and normality assumptions. Outliers may arise due to heterogenous variabilities. This results in a biased regression analysis. The sensitivity of the LS regression analysis to atypical values in the dependent and/or independent variables makes it difficult to identify outliers in a residual analysis. A robust regression method, least median squares (LMS), is insensitive to atypical values in the dependent and/or independent variables in a regression analysis. Thus, outliers that have significantly different variances from the rest of the data can be identified in a residual analysis. Using simulated and field data, we explore the application of LMS in the analysis of fisheries data. A two-step procedure is suggested in analyzing fisheries data.

scholarly journals Kebermaknaan Hidup, Kestabilan Emosi dan Depresi

2015 ◽  
Vol 4 (03) ◽  
Author(s):  
R Hendro Rumpoko Perwito Utomo ◽  
Tatik Meiyuntari
Keyword(s):  
Regression Analysis ◽  
Significant Relationship ◽  
Meaning In Life ◽  
Regression Line ◽  
Early Adulthood ◽  
Emotional Stability ◽  
Negative Coefficient ◽  
Line Equation ◽  
Depression Level ◽  
The Relationship

The purpose of this study was to examine the relationship between Meaningin Life and Emotional Stability at depression level. Subjects were students who enterthe category of early adulthood (19-25 years) in Yogyakarta. Number 93 researchsubjects. Regression analysis showed: (1) here was a significant relationship betweenemotional stability, Meaning in Life and Depression level , with a coefficient F =17.494 , p = 0.000 ; p < 0.05 ( 2 ) obtained regression line equation is Y = 106.2120421x1 - x2 0511 , where Y is the Depression , X1 and X2 are Meaning in Life isEmotional Stability . Negative coefficient in Meaning in Life and Stability Emotionsgive meaning or effect the opposite relationship with Depression . If Meaning in Lifeand Emotional Stability increases, the depression will decrease and vice versa if theMeaning in Life and Emotional Stability decreased it will increase the Depression

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

International association meetings

2017 ◽  
Vol 24 (3) ◽  
pp. 218-233 ◽  
Author(s):  
Susana Cró ◽  
António Miguel Martins
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Host Country ◽  
International Association ◽  
Ordinary Least Squares ◽  
Travel And Tourism ◽  
Meeting Planners ◽  
Competitiveness Index ◽  
Destination Management Organizations ◽  
Selection Of

The aim of our study is to discuss whether the key frequently identified destination attributes desired by associations and meeting planners determine the number of association meetings organized by each country in 2014. Regression analysis was used by ordinary least squares for the number of association meetings organized in 2014 by each country that shows the importance of 12 countries’ destination attributes reported in the meetings, incentives, conventions and events/exhibitions sector literature and included in the travel and tourism competitiveness index. Our study contributes to the literature in two ways: (i) to identify and evaluate the key attributes in the attraction of association meetings (until now dispersed) and (ii) empirically test the importance of these attributes in the selection of meeting host country. From a practical perspective, these findings give valuable information for destination management organizations and meeting planners about the factors that should be improved in each country in order to be selected more often in the organization of those events.

scholarly journals Evaluating Competition Constraint on Land Accessibility by Urban Crop Farmers in Lagos

2017 ◽  
Vol 1 (1) ◽  
pp. 90-98
Author(s):  
C. O. Odudu
Keyword(s):  
Regression Analysis ◽  
Agricultural Development ◽  
Missing Values ◽  
Linear Regression Analysis ◽  
Descriptive Statistics ◽  
Marginal Lands ◽  
Farming Communities ◽  
Multi Stage ◽  
Crop Farming ◽  
Lagos State

The study evaluated the constraint of competition on urban crop farming in Lagos with a view to identifying issues that must be resolved to facilitate practitioners’ land accessibility in the metropolis. Crop farmers in seven out of ten communities where urban crop farming was found to be thriving within the metropolis were selected through multi-stage sampling which involved both purposive and simple random samplings and were administered with structured questionnaires. All the farming communities were delineated by the Lagos State Agricultural Development Authority (LSADA). Data collected were analysed using descriptive statistics while linear regression analysis was used to test the formulated research hypothesis. The study showed that farmers were forced out (19.5%) of their locations, 10.3% vacated voluntarily, 1.4% left due to high rents, 2.9% unidentified and 67.8% were missing values. Urban farmers in the study area were, however, found not to be affected by competition and high rents as they were occupying marginal lands that did not attract other competing uses. The regression analysis showed that competition constraint accounted for 3.5% of farmers’ productivity establishing that competition with other uses significantly affected urban farmers’ productivity as they were consigned to marginal lands. The study therefore concluded that government should support/promote the activity by providing agricultural lands in designated areas of the metropolis for urban farming.

scholarly journals The Marketing Mix Affect on the Consumer Buying Decision (Case Study of Sausage Products at PT. Aroma Denpasar)

2017 ◽  
Vol 1 (2) ◽  
pp. 65
Author(s):  
I Made Jaminyasa ◽  
I Made Pulawan ◽  
Anak Agung Media Martadiani ◽  
I Made Suniastha Amerta
Keyword(s):  
Linear Regression ◽  
Product Quality ◽  
Regression Coefficient ◽  
Regression Line ◽  
Positive Influence ◽  
Marketing Mix ◽  
T Test ◽  
Rejection Region ◽  
Line Equation

The more intense competition within the similar business as well as happened in the business of making sausages, especially in Denpasar city. PT. Aroma was one of the companies in Denpasar that produces sausages, corned beef, and nuggets. In an effort to attract consumers to buy sausages, companies pay attention to product quality, price, and promotion. The attitude of each consumer varies before buying and in buying products. Consumer considerations in buying the products that need to be considered by marketers, so that products that are marketed can be accepted and would be bought by the consumers. The linear regression line equation: Y = 0.1920 + 0.2145 X1 + 0.2592 X2 + 0.3828 X3 explains that there was a simultaneous positive influence between product quality, price, and promotion on the buying decision of sausage. The result of t-test of regression coefficient obtained t1-count was 3,3628, t2-count was 3,9879 and t3-count was 6,2641 bigger than t-table equal to 1,980 was in rejection region Ho, hence Ho rejected or Hi accepted. It meant it was true, that there was a positive influence simultaneously between the marketing mix and the consumer buying decision.

scholarly journals APPLICATION OF REGRESSION ANALYSIS FOR CALCULATIONS UNDER DATA OF IKFS-2 AND MTVZA DEVICES OF METEOR-2 SATELLITES

2019 ◽  
Vol 4 (1) ◽  
pp. 3-10
Author(s):  
Feodor Pyatkin ◽  
Vladimir Golomolzin ◽  
Angelica Kostornaya
Keyword(s):  
Carbon Dioxide ◽  
Regression Analysis ◽  
Water Vapor ◽  
Carbon Dioxide Concentration ◽  
Regression Method ◽  
Atmospheric Parameters ◽  
Hyper Spectral ◽  
Microwave Sensor ◽  
Multiple Regression Method ◽  
Infrared Fourier Spectrometer

This article examines a multiple regression method to calculate the characteristics of the at-mosphere with hyper spectral sensors data of “Meteor-2” satellite. The method was used to calculate the carbon dioxide concentration by infrared Fourier-spectrometer IKFS-2 and the integral content of water vapor of atmosphere for the microwave sensor MTVZA-GY. Analysis of the obtained results and calculation errors showed the possibility of using this method to restore the values of atmospheric parameters. The proposed method could be applied to for various sensors and it allows to expand the capabilities of other methods.

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