Structural change detection in ordinal time series

Abstract

Change-point detection in health care data has recently obtained considerable attention due to the increased availability of complex data in real-time. In many applications, the observed data is an ordinal time series. Two kinds of test statistics are proposed to detect the structural change of cumulative logistic regression model, which is often used in applications for the analysis of ordinal time series. One is the standardized efficient score vector, the other one is the quadratic form of the efficient score vector with a weight function. Under the null hypothesis, we derive the asymptotic distribution of the two test statistics, and prove the consistency under the alternative hypothesis. We also study the consistency of the change-point estimator, and a binary segmentation procedure is suggested for estimating the locations of possible multiple change-points. Simulation results show that the former statistic performs better when the change-point occurs at the centre of the data, but the latter is preferable when the change-point occurs at the beginning or end of the data. Furthermore, the former statistic could find the reason for rejecting the null hypothesis. Finally, we apply the two test statistics to a group of sleep data, the results show that there exists a structural change in the data.

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“…Based on raw data from the global positioning network, Kaplan and Lau [8] analyzed structural data in different environments by using new high-resolution grid time series and lightning density climatology. To improve the optimization ability of complex data, Li et al [9] proposed two methods to test statistics based on relevant theoretical analysis. Based on the theory of time series and neural networks, the cumulative Logistic regression model can be used to analyze the ordered time series.…”