Change point detection by Filtered Derivative with p-Value : Choice of the extra-parameters
Résumé
This paper deals with off-line change point detection using the FDpV method. The Filtered Derivative with p-Value method (FDpV) is a two-step procedure for change point analysis. In the first step, we use the Filtered Derivative (FD) to select a set of potential change points, using its extra-parameters-namely the threshold for detection, and the sliding window size. In the second one, we calculate the p-value for each change point in order to only retain the true positives (true change points) and discard the false positives (false alarms). We give a way to estimate the optimal extra-parameters of the function FD, in order to have the fewest possible false positives and non-detected change points (ND). Indeed, the estimated potential change points may differ slightly from the theoretically correct ones. After setting the extra-parameters, we need to know whether the absence of detection or the false alarm has more impact on the Mean Integrated Square Error (MISE), which prompts us to calculate the MISE in both cases. Finally, we simulate some examples with a Monte-Carlo method to better understand the positive and negative ways the parametrisation can affect the results.
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Loading...