Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

Indonesian Operations Research Association - International Conference on Operations Research 2017

Gusriani, N.^1 ; Firdaniza^1

Department of Mathematics, Faculty of Mathematics and Science, Padjadjaran University, Jalan Raya Bandung-Sumedang Km 21, Jatinangor, Indonesia^1

关键词: Coefficient values;    Gaussian assumption;    Least square methods;    Minimum covariance determinant;    Multiple linear regression analysis;    Robust regressions;    Secondary datum;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/332/1/012037/pdf
DOI  :  10.1088/1757-899X/332/1/012037

来源: IOP

【 摘 要 】

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.