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All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Erfiani Erfiani
Program Studi Statistika Terapan, Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Analisis Ridge Robust Penduga Generalized M (GM) Pada Pemodelan Kalibrasi Untuk Kadar Gula Darah Agung Tri Utomo; Erfiani Erfiani; Anwar Fitrianto
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (384.666 KB) | DOI: 10.35580/variansiunm14

Abstract

Calibration modeling is one of the methods used to analyze the relationship between different methods. The relationship is like the relationship between invasive and non-invasive blood sugar measurement. Problems that often arise in calibration modeling are multicollinearity and outliers. Multicollinearity problems can cause the regression confidence interval to widen, so that there is no statistically significant regression coefficient. Outliers cause statistical tests to deviate. The handling of these problems can be solved by robust ridge analysis. Ridge robust is a combined analysis of ridge regression and robust regression. Ridge regression is able to overcome the problem of multicollinearity and robust regression can overcome the problem of outliers. The estimator used is Generalized M (GM). This method will be applied to a calibration model that uses invasive and non-invasive blood sugar level data. The model used with Generalized M (GM) estimator robust regression using modulation clusters 50 to 90 in 2017 is better than the modulation group 50. up to 90 in 2019. The statistical values obtained are SSE of 0.910, RMSEadj of 0.114, and RMSEP of 0.030. Calibration models that have outliers and multicollinearity problems can be overcome by robust ridge regression. The feasibility value of the model obtained in the GM estimator robust regression is smaller than the MM estimator ridge robust regression in the calibration modeling for non-invasive blood sugar level data. That is, the best model that can be used is the robust ridge regression GM estimator.
Co-Authors Agung Tri Utomo Anwar Fitrianto