dc.contributor.author | Lombard, F. | |
dc.contributor.author | Van Zyl, C. | |
dc.date.accessioned | 2017-10-10T07:51:35Z | |
dc.date.available | 2017-10-10T07:51:35Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Lombard, F. & Van Zyl, C. 2018. Signed sequential rank CUSUMs. Computational statistics and data analysis, 118:30-39. [https://doi.org/10.1016/j.csda.2017.08.007] | en_US |
dc.identifier.issn | 0167-9473 | |
dc.identifier.issn | 1872-7352 (Online) | |
dc.identifier.uri | http://hdl.handle.net/10394/25757 | |
dc.identifier.uri | https://doi.org/10.1016/j.csda.2017.08.007 | |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0167947317301871 | |
dc.description.abstract | CUSUMs based on the signed sequential ranks of observations are developed for detecting location and scale changes in symmetric distributions. The CUSUMs are distribution-free and fully self-starting: given a specified in-control median and nominal in-control average run length, no parametric specification of the underlying distribution is required in order to find the correct control limits. If the underlying distribution is normal with unknown variance, a CUSUM based on the Van der Waerden signed rank score produces out-of-control average run lengths that are commensurate with those produced by the standard CUSUM for a normal distribution with known variance. For heavier tailed distributions, use of a CUSUM based on the Wilcoxon signed rank score is indicated. The methodology is illustrated by application to real data from an industrial environment | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | CUSUM | en_US |
dc.subject | Distribution-free | en_US |
dc.subject | Self starting | en_US |
dc.subject | Signed sequential ranks | en_US |
dc.subject | Symmetric distributions | en_US |
dc.title | Signed sequential rank CUSUMs | en_US |
dc.type | Article | en_US |
dc.contributor.researchID | 12950149 - Lombard, Frederick | |