Please use this identifier to cite or link to this item: https://scholar.utcc.ac.th/handle/6626976254/3741
Title: A Comparison of One-Step-Ahead Prediction Methods for First-Order Autoregressive Model with Outliers
Authors: Panichkitkosolkul, Wararit 
Niwitpong, Suparat 
Issue Date: 2009
Publisher: Chulalongkorn University Printing House
University of the Thai Chamber of Commerce
Source: Wararit Panichkitkosolkul, Suparat Niwitpong (2009) A Comparison of One-Step-Ahead Prediction Methods for First-Order Autoregressive Model with Outliers. University of the Thai Chamber of Commerce Journal Vol.29 No.4.
Journal: University of the Thai Chamber of Commerce Journal 
Abstract: The objective of this research is to compare the one-step-ahead prediction methodsfor first-order autoregressive model with outliers. These methods are the predictionmethod using recursive mean OLS method (RM), using recursive median OLS method(RMD), and using improved recursive median OLS method (IRMD). The sample sizesare 25, 50, 100, and 250. The variance of random error (σa2) is equal to 1. Thepercentages of outliers are 5% and 10% and the magnitude of outliers is equal 3σato 5σa and. This research uses the Monte Carlo simulation method. The experimentwas repeated 10,000 times for each condition to calculate the prediction mean squareerror (PMSE). Results of the research are as follows: a RM method provides thelowest PMSE when ρ is not close to 1 in all situations. The PMSE of a RMD methodis lower than that of a RM method when ρ is close to 1 in almost all cases. A IRMD provides the lowest PMSE when ρ is close to 1 in all situations.
URI: https://scholar.utcc.ac.th/handle/6626976254/3741
ISSN: 0125-2437
Rights: This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Appears in Collections:JEO: Journal Articles

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