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On the Adaptive Partition Approach to the Detection of Multiple Change-Points

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  • Yinglei Lai

Abstract

With an adaptive partition procedure, we can partition a “time course” into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combination or partition procedures do not guarantee a global optimization. We propose a modified dynamic programming algorithm to achieve a global optimization. Our method can provide consistent estimation results. In a comprehensive simulation study, our method shows an improved performance when it is compared to the recursive combination/partition procedures. In practice, can be determined based on a cross-validation procedure. As an application, we consider the well-known Pima Indian Diabetes data. We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index and age.

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  • Yinglei Lai, 2011. "On the Adaptive Partition Approach to the Detection of Multiple Change-Points," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0019754
    DOI: 10.1371/journal.pone.0019754
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    Cited by:

    1. Yong Ma & Yinglei Lai & John M Lachin, 2014. "Identifying Change Points in a Covariate Effect on Time-to-Event Analysis with Reduced Isotonic Regression," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.

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