wAbstract
@Weights-of evidence (WofE) modeling and weighted logistic regression
(WLR) are two methods of regional mineral resource estimation,
which are closely related: For example, if all the map layers
selected for further analysis are binary and conditionally independent
of the mineral occurrences, expected WofE contrast parameters
are equal to WLR coefficients except for the constant term that
depends on unit area size. Although a good WofE strategy is supposed
to achieve approximate conditional independence, a common problem
is that the final estimated probabilities are biased. If there
are N deposits in a study area and the sum of all estimated probabilities
is written as S, then WofE generally results in SN. The difference
S - N can be tested for statistical significance. although WLR
yields S = N, WLR coefficients generally have relatively large
variances. Recently, several methods have been developed to obtain
WofE weights that either result in S = N, or become approximately
unbiased. A method that has not been applied before consists of
first performing WofE modeling and following this by WLR applied
to the weights. This method results in modified weights with unbiased
probabilities satisfying S = N. An additional advantage of this
approach is that it automatically copes with missing data on some
layers because weights of unit areas with missing data can be
set equal to zero as is generally practiced in WofE applications.
Some practical examples of application are provided.
Key Words: Weights-of-evidence; weighted logistic regression;
conditional independence; weights adjustmentx
Introduction
Weights-of-evidence
Weighted logistic regression
Examples of application
@Seafloor example
@Meguma Terrain example
Concluding remarks
Acknowledgments
References