Natural Resources Research

, Volume 20, Issue 4, pp 401–406 | Cite as

Comment on “A Conditional Dependence Adjusted Weights of Evidence Model” by Minfeng Deng in Natural Resources Research 18(2009), 249–258

Article

Notes

Acknowledgment

Instructive discussions on marginal odds with Prof. Peter E. Jupp, School of Mathematics and Statistics, University of St. Andrews, Scotland, are gratefully acknowledged.

References

  1. Agterberg, F. P. (2011). A modified weights-of-evidence method for regional mineral resource estimation. Natural Resources Research. doi: 10.1007/s11053-011-9138-0.
  2. Agterberg, F. P., & Bonham-Carter, G. F. (1999). Logistics regression and weights of evidence modeling in mineral exploration. In Proc. APCOM’99, computer applications in the minerals industries (pp. 583–590). Colorado School of Mines.Google Scholar
  3. Agterberg, F. P., Bonham-Carter, G. F., Wright, D. F., & Cheng, Q. (1989). Weights of evidence and weighted logistic regression for mineral potential mapping. In J. C. Davis & U. C. Herzfeld (Eds.), Computers in geology: 25 years of progress (pp. 13–32). New York: Oxford University Press.Google Scholar
  4. Deng, M. (2009). A conditional dependence adjusted weights of evidence model. Natural Resources Research, 18, 249–258. doi: 10.1007/s11053-009-9101-5.CrossRefGoogle Scholar
  5. Deng, M. (2010). Binary pattern recognition in the presence of correlated multiple dependent variables. Natural Resources Research, 19, 269–278. doi: 10.1007/s11053-010-9128-7 CrossRefGoogle Scholar
  6. R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0. http://www.R-project.or.

Copyright information

© International Association for Mathematical Geology 2011

Authors and Affiliations

  1. 1.Geoscience Mathematics and InformaticsTU BergakademieFreibergGermany
  2. 2.Applied StochasticsTU BergakademieFreibergGermany

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