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由于星地时间观测受大气层和地球表面环境影响,时间观测序列容易出现粗差,原子钟性能也可能出现相应异常扰动,需要对粗差进行分析处理.对此,本文引入基于识别变量的自回归(auto-regressive,AR)模型异常值探测的Bayesian方法对星地时间同步钟差序列中的异常值进行探测,进一步基于迭代似然比检验法中的异常值描述模型,将异常值估值问题转化为简单的线性模型最小二乘估计问题,以期对钟差序列中的异常值进行修复.实验表明本文的方法能够准确的探测出异常值的位置并精确的估计出异常值的大小.“,”Clock offset measurements of satellite-ground time transfer are usually affected by outliers due to the impact of ionosphere errors,tropospheric errors,and muhipath effects.Therefore,in this paper,we propose an autoregressive model based on Bayesian methods for detecting outliers in the clock offset measurements with the classification variables.Furthermore,the model for estimating the magnitude of outliers is given to correct the clock offset measurements,and solves the problem of outlier estimation by transforming it into a simple least square problem.Different schemes based on the real BDS data were designed to evaluate the performance of the new Bayesian method.We applied the new method ito the fast recovery of the clock offset prediction.Test examples illustrate that the Bayesian methods can detect the outliers effectively and estimate the magnitudes of outliers accurately.