Selection Criteria for Overlapping Binary Models—A Simulation Study

Abstract

This paper deals with the problem of choosing the optimum criterion for selecting the best model out of a set of overlapping binary models. The criteria we studied were the well-known AIC and SBIC, and a third one called C2. Special attention was paid to the setting where neither of the competing models was correctly specified. This situation has not been studied very much but it is the most common case in empirical works. The theoretical study we carried out allowed us to conclude that, in general terms, all criteria perform well. A Monte Carlo exercise corroborated those results.

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

“…These characteristics are matched with band 12 (2.100-2.280 µm) and band 11 (1.565-1.655 µm) of Sentinel-2, as well as band 7 (2.11-2.29 µm) and band 5 (1.57-1.65 µm) of Landsat-7, respectively. Therefore, the 11/12 band ratio of Sentinel-2 and 5/7 band ratio of Landsat-7 were used to map hydroxyl bearing altera- The multi-class overlap index method is an extended version of the binary index overlap modeling method [35][36][37][38][39]. Each of the j patterns (classes) of the i control map has a S ij weight to indicate the degree of dependence on mineral deposits.…”

“…The pattern of the inference engine would imitate the geospatial process being modeled [32]. The multi-class overlap index method is an extended version of the binary index overlap modeling method [35][36][37][38][39]. Each of the j patterns (classes) of the i control map has a Sij weight to indicate the degree of dependence on mineral deposits.…”