Robust Likelihood Calculation for Time Series
提出一种高效计算方法，用于在多个子模型下计算时间序列的似然，每个子模型假设存在异常值或水平偏移，适用于稳健估计和线性回归。
SUMMARY We propose a computationally efficient method for calculating the likelihoods of a time series under many submodels, each of which assumes a patch of outliers or level shifts. We assume a state space representation of the time series model with a Bayesian-type treatment of anomalies. The calculations form the basis for an efficient and robust estimation procedure. The method is also applicable to linear regression with correlated errors and is illustrated with two examples.