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Generalized linear spatial models to predict slate exploitability

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Publication:364424
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DOI10.1155/2013/531062zbMath1271.91085OpenAlexW2075609338WikidataQ59002860 ScholiaQ59002860MaRDI QIDQ364424

Mohammad Hasan, M. Dambrine

Publication date: 9 September 2013

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/531062


Mathematics Subject Classification ID

Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76)



Uses Software

  • R


Cites Work

  • Unnamed Item
  • Unnamed Item
  • ROC curve and covariates: extending induced methodology to the non-parametric framework
  • Support vector machines and gradient boosting for graphical estimation of a slate deposit
  • On Estimation and Prediction for Spatial Generalized Linear Mixed Models
  • Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
  • Childhood Malaria in the Gambia: A Case-Study in Model-Based Geostatistics
  • Model-Based Geostatistics
  • Bayesian Prediction of Transformed Gaussian Random Fields
  • Approximate Inference in Generalized Linear Mixed Models
  • Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
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This page was last edited on 30 January 2024, at 03:54.
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