Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98

The Determinants of Cloud Computing Adoption in Healthcare

Cloud computing is useful for the healthcare sector since it reduces complexity, enables efficientadministration, and facilitates collaboration between the systems in healthcare sectors. This research seeksto examine the factors affecting the adoption of cloud computing in healthcare. It used three robust leastsquare estimation techniques such as S-estimation, M-estimation, and MM-estimation. The findings suggestthat the determinants of adoption of cloud computing are similar to other business institutions such ascompatibility, technological preparedness, complexity, security, competitive constraints, savings on costs,assistance to senior management, vendor assistance.

Response Surface Model with Comparison of OLS Estimation and 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.

Robust SURE estimates of profitability in the Egyptian insurance market

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.

A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation

The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

THE EFFECT OF ECONOMIC STRENGTH, GOVERNMENT DEBT, LEVEL OF DEMOCRACY, PUBLIC TRUST, AND LEVEL OF HAPPINESS ON CORRUPTION PERCEPTION

This study aims to analyze the effect of economic strength, government debt, level of democracy, public trust in government, and level of happiness on corruption perception. Data consisting of 113 countries are used to determine the causal relationship between variables that have been collected. Robust Regression statistical test with Method of Moment (MM) estimation is used to analyze the relationship between variables. The test results show that economic strength, level of democracy, public trust, and level of happiness have a significant positive effect on corruption perception, while government debt has no significant effect on corruption perception. It can be concluded that the higher the economic strength, the level of democracy, the public trust, and the level of happiness, the higher the corruption perception in the country. High corruption perception indicates the cleanliness of the country from corruption.