Comment on “A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity-by Daojun Zhang, Frits Agterberg, Qiuming Cheng, and Renguang Zuo Math Geosci DOI 10.1007/s11004-013-

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• 作者：Helmut Schaeben
• 关键词：Potential modeling ; Targeting ; Missing data ; Mathematical modeling assumptions ; Conditional independence ; Simple mathematical model ; Parsimonious mathematical model ; Proper predictions
• 刊名：Mathematical Geosciences
• 出版年：2014
• 出版时间：October 2014
• 年：2014
• 卷：46
• 期：7
• 页码：887-893
• 全文大小：170 KB
• 参考文献：1. Agterberg FP (2011) A modified weights-of-evidence method for regional mineral resource estimation. Nat Resour Res 20:95-01
  2. Anscombe FJ (1973) Graphs in statistical analysis. Am Stat 27:17-1
  3. Deng M (2009) A conditional dependence adjusted weights of evidence model. Nat Resour Res 18:249-58
  4. Firth D (1993) Bias reduction of maximum likelihood estimates. Biometrika 80:27-8 CrossRef
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  6. Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, London CrossRef
  7. Schaeben H (2014a) A mathematical view of weights-of-evidence, conditional independence, and logistic regression in terms of Markov random fields. Math Geosci. doi:10.1007/s11004-013-9513-y
  8. Schaeben H (2014b) Potential modeling: conditional independence matters. GEM Int J Geomath 5:99-16. doi:10.1007/s13137-014-0059-z CrossRef
  9. Schaeben H, van den Boogaart KG (2011) Comment on “A conditional dependence adjusted weights of evidence model-by Minfeng Deng in Natural Resources Research 18(2009):249-58. Nat Resour Res 20:401-06
  10. Sutton C, McCallum A (2007) An introduction to conditional random fields for relational learning. In: Getoor L, Taskar B (eds) Introduction to statistical relational learning. MIT Press, USA, pp 93-27
  11. van den Boogaart KG, Schaeben H (2012) Mineral potential mapping using Coxtype regression for marked point processes. In: Proceeding of the 34th IGC Brisbane, Australia
  12. Zhang D, Agterberg F, Cheng Q, Zuo R (2013) A comparison of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression for mapping mineral prospectivity. Math Geosci. doi:10.1007/s11004-013-9496-8
  13. Zhang D, Cheng Q, Zuo R, Wang S (2012) Application and comparison of weighted weights of evidence models. Earth Sci J China Univ Geosci 37:1160-168 (in Chinese with English abstract)
  
• 作者单位：Helmut Schaeben (1)
  
  1. Department of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Freiberg, Germany
  
• ISSN：1874-8953

文摘

Despite a missing definition of equivalence of mathematical models or methods by Zhang et al. (Math Geosci, 2013), an “equivalence-(Zhang et al., Math Geosci, 2013, p. 6,7,8,14) of modified weights-of-evidence (Agterberg, Nat Resour Res 20:95-01, 2011) and logistic regression does not generally exist. Its alleged proof is based on a previously conjectured linear relationship between weights of evidence and logistic regression parameters (Deng, Nat Resour Res 18:249-58, 2009), which does not generally exist either (Schaeben and van den Boogaart, Nat Resour Res 20:401-06, 2011). In fact, an extremely simple linear relationship exists only if the predictor variables are conditionally independent given the target variable, in which case the contrasts, i.e., the differences of the weights, are equal to the logistic regression parameters. Thus, weights-of-evidence is the special case of logistic regression if the predictor variables are binary and conditionally independent given the target variable.