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© 2018

Quantitative Methods in Environmental and Climate Research

  • Michela Cameletti
  • Francesco Finazzi

Benefits

  • Provides an overview of some of the most recent and advanced statistical methods used to analyse climate and environmental data

  • Describes fascinating research applications in the context of the environmental sciences

  • Provides challenging and novel data sets involving complex spatio-temporal structures and obtained by various measuring instruments and models

Conference proceedings

Table of contents

  1. Front Matter
    Pages i-vii
  2. Khurram Nadeem, Entao Chen, Ying Zhang
    Pages 29-48
  3. Ilia Negri, Alessandro Fassò, Lucia Mona, Nikolaos Papagiannopoulos, Fabio Madonna
    Pages 63-83
  4. Yasmine M. Abdelfattah, Abdel H. El-Shaarawi, Hala Abou-Ali
    Pages 99-120

About these proceedings

Introduction

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. 



Keywords

spatio-temporal models geostatistics Bayesian modeling functional data analysis health risk uncertainty assessment climate change air pollution satelite earth and atmospheric data big data environmental epidemiology remote sensing cylindrical data LIDAR data weather forecast

Editors and affiliations

  • Michela Cameletti
    • 1
  • Francesco Finazzi
    • 2
  1. 1.Department of Management, Economics and Quantitative MethodsUniversity of BergamoBergamoItaly
  2. 2.Department of Management, Information and Production EngineeringUniversity of BergamoDalmineItaly

About the editors

Michela Cameletti is an Associate Professor of Statistics at the Department of Management, Economics and Quantitative Methods, University of Bergamo, Italy.  Her research interests include spatial and spatio-temporal models for environmental applications and computational methods for Bayesian inference.

 

Francesco Finazzi is researcher in Statistics at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. His research interests include spatio-temporal models, sensor networks and scientific software.

 


Bibliographic information