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| Content Provider | Springer Nature Link |
|---|---|
| Author | Bulla, J. Lagona, F. Maruotti, A. Picone, M. |
| Copyright Year | 2012 |
| Abstract | The identification of sea regimes from environmental multivariate times series is complicated by the mixed linear–circular support of the data, by the occurrence of missing values, by the skewness of some variables, and by the temporal autocorrelation of the measurements. We address these issues simultaneously by a hidden Markov approach, and segment the data into pairs of toroidal and skew-elliptical clusters by means of the inferred sequence of latent states. Toroidal clusters are defined by a class of bivariate von Mises densities, while skew-elliptical clusters are defined by mixed linear models with positive random effects. The core of the classification procedure is an EM algorithm accounting for missing measurements, unknown cluster membership, and random effects as different sources of incomplete information. Moreover, standard simulation routines allow for the efficient computation of bootstrap standard errors. The proposed procedure is illustrated for a multivariate marine time series, and identifies a number of wintertime regimes in the Adriatic Sea. |
| Starting Page | 544 |
| Ending Page | 567 |
| Page Count | 24 |
| File Format | |
| ISSN | 10857117 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume Number | 17 |
| Issue Number | 4 |
| e-ISSN | 15372693 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2012-09-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Circular data EM algorithm Hidden Markov model Model-based clustering Skewness Unsupervised classification Wave Wind Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty |
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