Slope estimation in replicated linear functional relationship model via trimmed mean: A simulation study

A nonparametric method using the trimmed mean is proposed to estimate the slope parameter of the replicated linear functional relationship model (LFRM). This study considers the replicated LFRM when the error terms are not assumed to have a normal distribution. The maximum likelihood estimation meth...

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Main Authors: Arif, Azuraini Mohd, Zubairi, Yong Zulina, Hussin, Abdul Ghapor
Format: Conference Proceeding
Language:English
Subjects:
Error analysis
Mathematical models
Maximum likelihood estimation
Normal distribution
Outliers (statistics)
Parameters
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Zubairi, Yong Zulina
Hussin, Abdul Ghapor
description A nonparametric method using the trimmed mean is proposed to estimate the slope parameter of the replicated linear functional relationship model (LFRM). This study considers the replicated LFRM when the error terms are not assumed to have a normal distribution. The maximum likelihood estimation method requires the assumption of normality and may lead to errors when outliers are present in the data. The performance of the proposed method is compared with the maximum likelihood estimation method in terms of estimated bias and mean square error. Results simulation study highlighted that the proposed method provides a better estimate of the slope parameter in the presence of outliers as compared to the maximum likelihood estimation method.
doi_str_mv 10.1063/5.0192108
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Error analysis
Mathematical models
Maximum likelihood estimation
Normal distribution
Outliers (statistics)
Parameters
title Slope estimation in replicated linear functional relationship model via trimmed mean: A simulation study
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