On Robust Change Point Detection and Estimation in Multisubject Studies

Yana Melnykov () and Marcus Perry
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Yana Melnykov: The University of Alabama
Marcus Perry: The University of Alabama

Sankhya A: The Indian Journal of Statistics, 2024, vol. 86, issue 2, No 7, 827-879

Abstract: Abstract A variety of change point estimation and detection algorithms have been developed for random variables observed over time. The acquisition of data in current practice often results in multiple subjects studied. The traditional treatment of such observations involves the assumption of their independence. In practice, however, this assumption is often inadequate or unrealistic. We propose an effective and modern computerized approach to estimating and detecting change points in linear model time series processes in the situation when the assumption of independent observations is not feasible. The developed methodology relies on the multivariate transformation and matrix normal distribution. The latter is used for separating the sources of variability. The application of the back-transform of the exponential transformation leads to a flexible distribution that effectively accounts for deviations from normality. The developed procedure has been successfully tested in various settings and applied to a crime rate data set.

Keywords: Change point; Matrix normal distribution; Manly transformation; Model selection; BIC; 62H99 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13171-024-00355-9

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