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

Utilization of fluorescence images in chlorophyll in Cirebon waters, Indonesia

Satellite is one of the tools used to detect chlorophyll concentration. MODIS chlorophyll concentrations appears to be disturbed by colored dissolved organic matter (CDOM). The fluorescence approach can represent the chlorophyll concentration near the coast more accurately. The data for this study was obtained from satellite Aqua MODIS Level 2 which consisted of MODIS chlorophyll, MODIS fluorescence data, and Observation data. The data was taken on 6 September 2020 in Cirebon Waters. Results of the chlorophyll concentration field data ranged from 0.64 mg m-³ - 4.26 mg m-³. Estimation of chlorophyll concentrations using the standard chlorophyll method ranged from 2.55 mg m-³ - 7.20 mg m-³ and the chlorophyll concentrations using the fluorescence method were 2.58 mg m-³ - 3.5 mg m-³. Comparison of field data with satellite images is better with the florescence method than the standard MODIS chlorophyll technique, with an error of 47.8% for fluorescence and 235.5% for the standard MODIS chlorophyll.

A COMPARISON OF M-ESTIMATION AND S-ESTIMATION ON THE FACTORS AFFECTING IR DHF IN EAST JAVA IN 2017

ABSTRACTRobust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of Dengue Haemoragic Fever (DHF) in East Java has been influenced by climate, population density, human behavior, and environmental sanitation. This study aimed to compare robust regression research by using M estimation and S estimation on the factors that affect IR DHF. This was done to get the best model regression on the data outlier based on the biggest R2 adjusted and the smallest MSE. This study utitlized observational research with a non-reactive research design using secondary data. The independent variable consisted of population density, healthy behavior, healthy living environment house, and precipitation in East Java in 2017. The dependent variable was incident rate of DHF in 2017. The population included 38 regencies in East Java, while the sample was 35 regencies/cities selected using simple random sampling. The analysis used robust regression on M estimation and S estimation weighting by Tukey’s Bisquare. Robust regression on S estimation was found to be the best robust regression on data outlier with R2 adjusted (0.996) and MSE (0.229). Robust regression on S estimation was = 54.826 + 0.011 (population density) – 0.136 (% healthy behavior) - 0,404 (% healthy house ) - 0,005 (precipitation).Some factors that affect IR DHF can be the main focus for the prevention and control of DHF for the government and society. Keywords: robust regression, outlier, estimation, estimation, DHF

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.

Robust and Nonrobust Linking of Two Groups for the Rasch Model with Balanced and Unbalanced Random DIF: A Comparative Simulation Study and the Simultaneous Assessment of Standard Errors and Linking Errors with Resampling Techniques

In this article, the Rasch model is used for assessing a mean difference between two groups for a test of dichotomous items. It is assumed that random differential item functioning (DIF) exists that can bias group differences. The case of balanced DIF is distinguished from the case of unbalanced DIF. In balanced DIF, DIF effects on average cancel out. In contrast, in unbalanced DIF, the expected value of DIF effects can differ from zero and on average favor a particular group. Robust linking methods (e.g., invariance alignment) aim at determining group mean differences that are robust to the presence of DIF. In contrast, group differences obtained from nonrobust linking methods (e.g., Haebara linking) can be affected by the presence of a few DIF effects. Alternative robust and nonrobust linking methods are compared in a simulation study under various simulation conditions. It turned out that robust linking methods are preferred over nonrobust alternatives in the case of unbalanced DIF effects. Moreover, the theory of M-estimation, as an important approach to robust statistical estimation suitable for data with asymmetric errors, is used to study the asymptotic behavior of linking estimators if the number of items tends to infinity. These results give insights into the asymptotic bias and the estimation of linking errors that represent the variability in estimates due to selecting items in a test. Moreover, M-estimation is also used in an analytical treatment to assess standard errors and linking errors simultaneously. Finally, double jackknife and double half sampling methods are introduced and evaluated in a simulation study to assess standard errors and linking errors simultaneously. Half sampling outperformed jackknife estimators for the assessment of variability of estimates from robust linking methods.

Better the Devil You Know: Improved Forecasts from Imperfect Models

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis-specified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we find significant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.

Robust and Nonrobust Linking of Two Groups for the Rasch Model with Balanced and Unbalanced Random DIF: A Comparative Simulation Study and the Simultaneous Assessment of Standard Errors and Linking Errors with Resampling Techniques

In this article, the Rasch model is used for assessing a mean difference between two groups for a test of dichotomous items. It is assumed that random differential item functioning (DIF) exists that has the potential to bias group differences. The case of balanced DIF is distinguished from the case of unbalanced DIF. In balanced DIF, DIF effects cancel out on average. In contrast, in unbalanced DIF, the expected value of DIF effects can differ from zero and favors a particular group on average. Robust linking methods (e.g., invariance alignment) aim at determining group mean differences that are robust to the presence of DIF. In contrast, group differences obtained from nonrobust linking methods (e.g., Haebara linking) can be affected by the presence of a few DIF effects. Alternative robust and nonrobust linking methods are compared in a simulation study under various simulation conditions. It turned out that robust linking methods are preferred over nonrobust alternatives in the case of unbalanced DIF effects. Moreover, M-estimation theory is used for studying the asymptotic behavior of linking estimators if the number of items tends to infinity. These results give insights into asymptotic bias and the estimation of linking errors that represent the variability in estimates due to selecting items in a test. Moreover, M-estimation theory is also used in an analytical treatment to assess standard errors and linking errors simultaneously. Finally, double jackknife and double half sampling methods are introduced and evaluated in a simulation study to assess standard errors and linking errors simultaneously. Half sampling outperformed jackknife estimators for the assessment of variability of estimates from robust linking methods.