Guttman and Tiao [1], and Chang [2] showed that the effect of outliers may cause serious bias in estimating autocorrelations, partial correlations, and autoregressive moving average parameters (cited in Chang et al. [3]). This paper presents a modified weighted symmetric estimator for a Gaussian first-order autoregressive AR(1) model with additive outliers. We apply the recursive median adjustment based on an exponentially weighted moving average (EWMA) to the weighted symmetric estimator of

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... k and Fuller [4]. We consider the following estimators: the weighted symmetric estimator (ˆ ρ W), the recursive mean adjusted weighted symmetric estimator (ˆ ρ RW) proposed by Niwitpong [5], the recursive median adjusted weighted symmetric estimator (ˆ ρ RDW) proposed by Panichkitkosolkul [6], and the weighted symmetric estimator using adjusted recursive median based on EWMA (ˆ ρ − RD EWMA). Using Monte Carlo simulations, we compare the mean square error (MSE) of estimators. Simulation results have shown that the proposed estimator, ˆ ρ − RD EWMA , provides a MSE lower than those ofˆρofˆ ofˆρ W , ˆ ρ RW andˆρandˆ andˆρ RDW for almost all situations.