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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Electronics and Communications Systems
Netti Herawati
University of Lampung
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Modeling with generalized linear model on covid-19: Cases in Indonesia Subian Saidi; Netti Herawati; Khoirin Nisa
International Journal of Electronics and Communications Systems Vol 1, No 1 (2021): International Journal of Electronics and Communications System
Publisher : Raden Intan State Islamic University of Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.202 KB) | DOI: 10.24042/ijecs.v1i1.9299

Abstract

The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distribution. Where the three distributions will be compared to get the best model. The variables used in this study were the number of confirmed Covid-19 cases per day and the number of deaths due to Covid-19 per day. This study also aims to see how much influence the confirmation of Covid-19 has on the number of deaths due to Covid-19 per day. By using 3 types of exponential family distribution, the best result is the Gaussian distribution GLM. Selection of the best model using Akaike Information Criterion (AIC).
EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PONTENSIAL PENCILAN Netti Herawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1010.613 KB) | DOI: 10.30598/barekengvol14iss2pp305-312

Abstract

Quantile regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimates such as the presence of potential outliers in the data. The purpose of this study was to evaluate the effectiveness of quantile regression in dealing with potential outliers in multiple linear regression compared to ordinary least square (OLS). This study used simulation data in multiple regression model with the number of independent variables (p=3) for different sample sizes (n = 20, 40, 60, 100, 200) and and repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β parameters was measured by Mean square error (MSE) and the best model is chosen based on the smallest Akaike Information Criterion (AIC) value. The results showed that in contrast to OLS, quantile regression was able to deal with potential outliers and provided a better estimator with a smaller mean mean square error. Compared to OLS and other quantiles, this study also provides sufficient results that quantile 0.5 provides the best parameter estimate and the best model based on the smallest MSE and AIC values.
Co-Authors Khoirin Nisa Saidi, Subian