Cross efficiency evaluation was developed as an extension of DEA. But the traditional DEA models usually have alternative optimal solutions and, as a result, cross efficiency scores may not be unique. It is recommended that without changing the DEA efficiency scores, the secondary goal should be introduced for optimization of the inputs/outputs weights.  Several reports evaluated the perfo...

Recently, some authors proposed the use of symmetric weightsfor computing the elements of cross-efficiency matrix. In spite ofthe fact that the proposed method decreases the number of zeroweights, a large number of zero weights may still exist among inputand output symmetric weights. To decrease the number of input andoutput symmetric weights, this paper improves the proposed secondarygoal mode...

Haze is an inevitable interference when mapping land use/cover with optical satellite imagery. In this study, we applied weights of evidence model to locate possible area with omission and commission errors and estimate the degree of uncertainty for image classification in haze-contaminated area. Agricultural area in Kansas, U. S. is chosen as the study area. We used Landsat-5 TM images and hyb...

The plausibility of competing statistical models may be assessed using penalized log-likelihood criteria such as the AIC, which is given by AIC = −2lnL + 2k (L being the maximum likelihood estimate and k the number of free parameters). The raw AIC values can be transformed to AIC model weights by wi = exp(− 2∆AICi)/ ∑R r=1 exp(− 2∆AICr), where ∆AICi = AICi − min(AIC) and R is the total number o...

This paper proposed a novel method of decision fusion based on weights of evidence model (WOE). The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The r...

Linear models that form the basis for survey regression estimation and the conditions under which the regression estimators are design consistent are reviewed. Model justification for some commonly used regression estimators is presented. Test for reduced models against design consistent models are discussed.