Confidence Intervals for the Parameter of a Gaussian First-Order Autoregressive Model with Additive Outliers: A Simulation Study

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Wararit Panichkitkosolkul
Luckhana Saothayanun
Yupin Kanjanasakda
Sunee Taweesakulvatchara

Abstract

This paper is concerned with interval estimation of a parameter for a Gaussian first-order autoregressive model, AR(1), when there are additive outliers in a time series. We compared the confidence intervals basedon the weighted symmetric estimator ( ˆφW), the recursive mean adjusted weighted symmetric estimator (ˆφRW),the recursive median adjusted weighted symmetric estimator ( ˆφRDW ), and the improved recursive medianadjusted weighted symmetric estimator (ˆφIRDW) by using Monte Carlo simulation. Simulation results haveshown that the confidence interval based on the estimator ˆIRDW φ is better than the other confidence intervalswith respect to the coverage probability and the length criteria.Key Words: AR(1) model; Additive outliers; Confidence interval; Coverage probability; LengthIntroductionIn time series analysis, outliers or atypicalobservations can

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How to Cite
Panichkitkosolkul, W., Saothayanun, L., Kanjanasakda, Y., & Taweesakulvatchara, S. (2013). Confidence Intervals for the Parameter of a Gaussian First-Order Autoregressive Model with Additive Outliers: A Simulation Study. Science, Engineering and Health Studies, 6(1), 23–41. https://doi.org/10.14456/sustj.2012.2
Section
Research Articles

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