GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • English  (3)
  • Mathematics  (3)
Material
Publisher
Person/Organisation
Language
  • English  (3)
Years
Subjects(RVK)
  • Mathematics  (3)
RVK
  • 1
    Online Resource
    Online Resource
    A Spatiotemporal Auto-Regressive Moving Average Model for Solar Radiation
    Oxford University Press (OUP) ; 2008
    In:  Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 57, No. 3 ( 2008-06-01), p. 343-355
    Details
    In: Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 57, No. 3 ( 2008-06-01), p. 343-355
    Abstract: To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.
    Type of Medium: Online Resource
    ISSN: 0035-9254 , 1467-9876
    URL: Article
    DOI: 10.1111/j.1467-9876.2007.00617.x
    RVK:
    SA 7810
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2008
    detail.hit.zdb_id: 204797-4
    detail.hit.zdb_id: 1482300-7
    detail.hit.zdb_id: 1476894-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    A Latent Gaussian Markov Random-Field Model for Spatiotemporal Rainfall Disaggregation
    Oxford University Press (OUP) ; 2003
    In:  Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 52, No. 4 ( 2003-10-01), p. 487-498
    Details
    In: Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 52, No. 4 ( 2003-10-01), p. 487-498
    Abstract: Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatiotemporal model which allows multiple imputation of rainfall at fine spatial resolutions, with a realistic dependence structure in both space and time and with the total rainfall at the coarse scale consistent with that observed. The method involves the transformation of the fine scale rainfall to a thresholded Gaussian process which we model as a Gaussian Markov random field. Gibbs sampling is then used to generate realizations of rainfall efficiently at the fine scale. Results compare favourably with previous, less elegant methods.
    Type of Medium: Online Resource
    ISSN: 0035-9254 , 1467-9876
    URL: Article
    DOI: 10.1111/1467-9876.00419
    RVK:
    SA 7810
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2003
    detail.hit.zdb_id: 204797-4
    detail.hit.zdb_id: 1482300-7
    detail.hit.zdb_id: 1476894-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Maximum a Posteriori Estimation of Image Boundaries by Dynamic Programming
    Oxford University Press (OUP) ; 2002
    In:  Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 51, No. 2 ( 2002-05-01), p. 209-221
    Details
    In: Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 51, No. 2 ( 2002-05-01), p. 209-221
    Abstract: We seek a computationally fast method for solving a difficult image segmentation problem: the positioning of boundaries on medical scanner images to delineate tissues of interest. We formulate a Bayesian model for image boundaries such that the maximum a posterioriestimator is obtainable very efficiently by dynamic programming. The prior model for the boundary is a biased random walk and the likelihood is based on a border appearance model, with parameter values obtained from training images. The method is applied successfully to the segmentation of ultrasound images and X-ray computed tomographs of sheep, for application in sheep breeding programmes.
    Type of Medium: Online Resource
    ISSN: 0035-9254 , 1467-9876
    URL: Article
    DOI: 10.1111/1467-9876.00264
    RVK:
    SA 7810
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2002
    detail.hit.zdb_id: 204797-4
    detail.hit.zdb_id: 1482300-7
    detail.hit.zdb_id: 1476894-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...