NDLI: A Conditional Dependence Adjusted Weights of Evidence Model

Content Provider	Springer Nature Link
Author	Deng, Minfeng
Copyright Year	2009
Abstract	One of the key assumptions in weights of evidence (WE) modelling is that the predictor patterns have to be conditionally independent. When this assumption is violated, WE posterior probability estimates are likely to be biased upwards. In this paper, a formal expression for the bias of the contrasts will be derived. It will be shown that this bias has an intuitive and convenient interpretation. A modified WE model will then be developed, where the bias is corrected using the correlation structure of the predictor patterns. The new model is termed the conditional dependence adjusted weights of evidence (CDAWE) model. It will be demonstrated via a simulation study that the CDAWE model significantly outperforms the existing WE model when conditional independence is violated, and it is on par with logistic regression, which does not assume conditional independence. Furthermore, it will be argued that, in the presence of conditional dependence between predictor patterns, weights variance estimates from WE are likely to understate the true level of uncertainty. It will be argued that weights variance estimates from CDAWE, which are also bias-corrected, can properly address this issue.
Starting Page	249
Ending Page	258
Page Count	10
File Format	PDF
ISSN	15207439
Journal	Natural Resources Research
Volume Number	18
Issue Number	4
e-ISSN	15738981
Language	English
Publisher	Springer US
Publisher Date	2009-09-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Conditional independence weights of evidence logistic regression bias correction Hydrogeology Sedimentology Economic Geology Mathematical Applications in Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Environmental Science

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