Robust Methods for Recursive Autoregressive Moving Average Estimation
研究了在时间序列数据可能存在异常值时，如何递归地拟合自回归移动平均模型，提出了稳健化的递归最大似然方法，并通过模拟比较了自适应版本在非平稳情况下的表现。
SUMMARY In this paper we consider the problem of fitting, recursively in time, an autoregressive moving average (ARMA) model to time series data where outliers may be present. Although many recursive estimation procedures are available for fitting ARMA models, they are based on the recursive least squares algorithm which is known to be badly affected by additive outliers. to minimize the impact of these sort of outliers we investigate some robustified versions of the recursive maximum likelihood procedure. The problem of incorporating robust scale recursion is discussed and asymptotic properties of the procedures are examined. We also present results from simulation studies which compare adaptive versions of these procedures in the non-stationary case.