Abstract
For detecting an abrupt change when observations are continuous and independent, classical methods assume that the observations belong to a parametric family. Nonparametric methods have been devised, but usually they do not guarantee asymptotic optimality at a parametric model of choice. This article presents a nonparametric changepoint detection approach that guarantees compliance with a prespecified lower bound on the ARL to false alarm whatever the underlying distribution is, yet promises asymptotic first-order optimality if the true underlying distribution belongs to a suspected parametric family.
Original language | English |
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Pages (from-to) | 146-161 |
Number of pages | 16 |
Journal | Sequential Analysis |
Volume | 29 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2010 |
Keywords
- Changepoint detection
- Control chart
- Sequential analysis
- Spc