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All Journal Science and Technology Indonesia Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Sarmada Sarmada
Andalas University
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Education Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : Science and Technology Indonesia

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Quantile Regression Approach to Model Censored Data Sarmada Sarmada; Ferra Yanuar
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (929.748 KB) | DOI: 10.26554/sti.2020.5.3.79-84

Abstract

Abstract The censored quantile regression model is derived from the censored model. This method is used to overcome problems in modeling censored data as well as to overcome the assumptions of linear models that are not met. The purpose of this study is to compare the results of the analysis of the quantile regression method with the censored quantile regression method for censored data. Both methods were applied to generated data of 150, 500, and 3000 sample size. The best model is then chosen based on the smallest absolute bias and the smallest standard error as an indicator of the goodness of the model. This study proves that the censored quantile regression method tends to produce smaller absolute bias and a smaller standard error than the quantile regression method for all three group data specified. Thus it can be concluded that the censored quantile regression method could result in acceptable model for censored data. Keywords: Censored data; quantile regression; quantile regression censored; standard error; absolute bias.
Co-Authors Devianto, Dodi Syamsyida Rozi Yanuar, Ferra Yurinanda, Sherli