| dc.contributor.author | Hawkins, Douglas M. | |
| dc.contributor.author | Lombard, F. | |
| dc.date.accessioned | 2016-09-02T09:32:24Z | |
| dc.date.available | 2016-09-02T09:32:24Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | Hawkins, D.M. & Lombard, F. 2015. Segmentation of circular data. Journal of applied statistics, 42(1):88-97. [http://dx.doi.org/10.1080/02664763.2014.934665] | en_US |
| dc.identifier.issn | 0266-4763 | |
| dc.identifier.issn | 1360-0532 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/10394/18518 | |
| dc.identifier.uri | http://dx.doi.org/10.1080/02664763.2014.934665 | |
| dc.identifier.uri | http://www.tandfonline.com/doi/full/10.1080/02664763.2014.934665 | |
| dc.description.abstract | Circular data – data whose values lie in the interval [0,2π) – are important in a number of application areas. In some, there is a suspicion that a sequence of circular readings may contain two or more segments following different models. An analysis may then seek to decide whether there are multiple segments, and if so, to estimate the changepoints separating them. This paper presents an optimal method for segmenting sequences of data following the von Mises distribution. It is shown by example that the method is also successful in data following a distribution with much heavier tails | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor & Francis | en_US |
| dc.subject | Von Mises distribution | en_US |
| dc.subject | changepoints | en_US |
| dc.subject | model selection | en_US |
| dc.title | Segmentation of circular data | en_US |
| dc.type | Article | en_US |
| dc.contributor.researchID | 12950149 - Lombard, Frederick | |
