Retrospective change detection for binary time series models

Fokianos, K. and Gombay, E. and Hussein, A. (2014) Retrospective change detection for binary time series models. Journal of Statistical Planning and Inference, 145. pp. 102-112. ISSN 0378-3758

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Abstract

Detection of changes in health care performance, financial markets, and industrial processes have recently gained momentum due to the increased availability of complex data in real-time. As a consequence, there has been a growing demand in developing statistically rigorous methodologies for change-point detection in various types of data. In many practical situations, the data being monitored for the purpose of detecting changes are autocorrelated binary time series. We propose a new statistical procedure based on the partial likelihood score process for the retrospective detection of change in the coefficients of a logistic regression model with AR(p)-type autocorrelations. We carry out some Monte Carlo experiments to evaluate the power of the detection procedure as well as its probability of false alarm (type I error). We illustrate the utility using data on 30-day mortality rates after cardiac surgery and to data on IBM share transactions.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Planning and Inference
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
127757
Deposited By:
Deposited On:
26 Sep 2018 07:42
Refereed?:
Yes
Published?:
Published
Last Modified:
18 Mar 2020 07:26