EnviroStat: Statistical analysis of environmental space-time processes. Companion code to the book by Nhu D Le and James V Zidek, Springer (2006). ”Statistical analysis of environmental space-time processes”: The book is devoted to space-time modeling of data, e.g., several pollutants measured in time by a network of gauges located in space. The book is divided into four parts: Environmental processes, Space-time modeling, Design and risk assessment, Implementation. The first part is devoted to the main problems that can be encountered when studying problems in environmental sciences together with models that are usually applied to get relevant information. The second part is devoted to techniques that are known as kriging. The third part presents the fully general multivariate theory that may be used to design networks for monitoring environmental processes. The fourth part shows how to use the theory in practice. With the help of software, more specifically R codes, values at ungauged sites are estimated and potential new monitoring sites are selected. The mathematical theory for the suggested methods is explained in the Appendices.

References in zbMATH (referenced in 25 articles )

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  1. Al-Thani, Hessa; Lee, Jon: An R package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data (2020)
  2. Hessa Al-Thani, Jon Lee: An R Package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data (2020) arXiv
  3. Melo, Carlos E.; Mateu, Jorge; Melo, Oscar O.: A distance-based method for spatial prediction in the presence of trend (2020)
  4. Maharaj, Elizabeth Ann; Teles, Paulo; Brito, Paula: Clustering of interval time series (2019)
  5. Tsyrulnikov, Michael; Rakitko, Alexander: A hierarchical Bayes ensemble Kalman filter (2017)
  6. Bohorquez, Martha; Giraldo, Ramón; Mateu, Jorge: Optimal sampling for spatial prediction of functional data (2016)
  7. Lilleborge, Marie; Hauge, Ragnar; Eidsvik, Jo: Information gathering in Bayesian networks applied to petroleum prospecting (2016)
  8. Mark A. Wolters: A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems (2015) not zbMATH
  9. Zidek, James V.; Shaddick, Gavin; Taylor, Carolyn G.: Reducing estimation bias in adaptively changing monitoring networks with preferential site selection (2014)
  10. Bhattacharjya, Debarun; Eidsvik, Jo; Mukerji, Tapan: The value of information in portfolio problems with dependent projects (2013)
  11. Du, Juan; Ma, Chunsheng; Li, Yang: Isotropic variogram matrix functions on spheres (2013)
  12. Lee, Jon: Techniques for submodular maximization (2013)
  13. Uciński, Dariusz: An optimal scanning sensor activation policy for parameter estimation of distributed systems (2013)
  14. Villalta, Desirée; Guenni, Lelys; Rubio-Palis, Yasmin; Ramírez Arbeláez, Raúl: Bayesian space-time modeling of malaria incidence in Sucre state, Venezuela: spatial special issue (2013)
  15. Alonso, Francisco Javier; Bueso, María del Carmen; Angulo, José Miguel: Effect of data transformations on predictive risk indicators (2012)
  16. Arima, Serena; Cretarola, Lorenza; Lasinio, Giovanna Jona; Pollice, Alessio: Bayesian univariate space-time hierarchical model for mapping pollutant concentrations in the municipal area of Taranto (2012)
  17. Kazianka, Hannes; Pilz, Jürgen: Objective Bayesian analysis of spatial data with uncertain nugget and range parameters (2012)
  18. Angulo, J. M.; Madrid, A. E.; Ruiz-Medina, M. D.: Entropy-based correlated shrinkage of spatial random processes (2011)
  19. Kaplan, Alexey: Discussion of: “A statistical analysis of multiple temperature proxies: are reconstructions of surface temperatures over the last 1000 years reliable?” (2011)
  20. Bhattacharjya, Debarun; Eidsvik, Jo; Mukerji, Tapan: The value of information in spatial decision making (2010)

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