Publication details
Joint segmentation of multivariate Gaussian processes using mixed linear models.
| Authors | |
|---|---|
| Year of publication | 2011 |
| Type | Article in Periodical |
| Magazine / Source | Computational Statistics & Data Analysis |
| MU Faculty or unit | |
| Citation | |
| Web | Odkaz na stiahnutie článku |
| Doi | http://dx.doi.org/10.1016/j.csda.2010.09.015 |
| Field | Applied statistics, operation research |
| Keywords | Segmentation; Mixed linear model; Multivariate Gaussian process; Dynamic programming; EM algorithm |
| Description | The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. Applications are presented in the field of climatic data analysis. |