On Robust Change Point Detection and Estimation in Multisubject Studies

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 independe...

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Bibliographic Details
Published inSankhya. Series. A Vol. 86; no. 2; pp. 827 - 879
Main Authors Melnykov, Yana, Perry, Marcus
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.08.2024
Subjects
Mathematics and Statistics
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Change point
Manly transformation
Matrix normal distribution
62H99
Model selection
BIC
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Summary: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.
ISSN:0976-836X
0976-8378
DOI:10.1007/s13171-024-00355-9