rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Quantitative Methods in Environmental and Climate Research
  name:
    name part: Cameletti, Michela.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Finazzi, Francesco.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VII, 136 p. 30 illus., 23 illus. in color. online resource.
  abstract: This books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty sources and propagation. The topics covered include: detecting disease clusters, analysing harvest data, change point detection in ground-level ozone concentration, modelling atmospheric aerosol profiles, predicting wind speed, precipitation prediction and analysing spatial cylindrical data. The volume presents revised versions of selected contributions submitted at the joint TIES-GRASPA 2017 Conference on Climate and Environment, which was held at the University of Bergamo, Italy. As it is chiefly intended for researchers working at the forefront of statistical research in environmental applications, readers should be familiar with the basic methods for analysing spatial and spatio-temporal data. .
  table of contents: 1 Fast Bayesian classification for disease mapping and the detection of disease clusters -- 2 A Novel Hierarchical Multinomial Approach to Modelling Age-specific Harvest Data -- 3 Detection of change points in spatiotemporal data in presence of outliers and heavy-tailed observations -- 4 Modelling spatiotemporal mismatch for Aerosol profiles -- 5 A SPATIOTEMPORAL APPROACH FOR PREDICTING WIND SPEED ALONG THE COAST OF VALPARAISO, CHILE -- 6 Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin: 1998-2016 -- 7 A hidden Markov random field with copula-based emission distributions for the analysis of spatial cylindrical data.
  note: edited by Michela Cameletti, Francesco Finazzi.
  subject:
    topic: Statistics
  subject:
    topic: Ecology
  subject:
    topic: Climate change
  subject:
    topic: Environmental monitoring
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Theoretical Ecology/Statistics
  subject:
    topic: Climate Change
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Climate Change/Climate Change Impacts
  subject:
    topic: Monitoring/Environmental Analysis
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783030015848
  identifier: https://doi.org/10.1007/978-3-030-01584-8
  record info:
    record creation date: 181230
    record change date: 20191022172823.0
    record identifier: 978-3-030-01584-8