Online monitoring of mean change point in a random coefficient autoregressive model
随机系数自回归模型变均值点在线监测与应用

In online monitoring the varying mean point of a random coefficient autoregressive model, if the varied mean point is far in position from the starting point of monitoring, it will take longer operation time in average to detect that varied mean point. To deal with this problem, we propose an improved procedure by introducing a window-width parameter. The asymptotic distribution of the monitoring statistic under null hypothesis is derived and its consistency is proved. Simulations show that our method is more powerful than the existing ones. This method has been applied to two groups of stock data for monitoring the variations of the mean points; results validate the effectiveness of the proposed procedure.