The group SCAD successfully removed the false change detected by the GLR at sample 3400.
It seems logical that the amount of false change is influenced by the degree of fragmentation of the landscape.
Labeled agents of change included urbanization, forest management, and natural change (largely fire), as well as labels associated with spectral change that was non-informative (false change).
False change was positively correlated with the mean nearest neighbour distance between forest fragments, the total forested area and the number of forest patches present.
The results show that, when the positional error is kept constant, landscapes with a different degree of forest fragmentation contain diverging amounts of false change.
The spatial pattern of forest cover was quantified by means of pattern metrics and the relation between false change and spatial pattern was analyzed using a stepwise regression procedure.
The estimated d ^ n 2 over time is displayed in Figure 8. Observe that the group Lasso captures the correct changes at time instants n ^ 1 = 1632 and n ^ 2 = 3155 along with a small false change at time instant n f = 2770.
The charges, contradictory and false, changed several times as the investigation went on.
When the SNR was reduced to 4 in simulation 3, the fold-change method with 1.5 folds picks 85% true positives equivalent to 894 true change points but the number of false positives also increased dramatically to 695 false change points (20%FP), which is too high for practical purposes while the Bayesian model detected 815 true positives and 182 false positives (5%FP).
Table 1 shows the average and standard deviation of the false-change rate that was calculated for each of the 10 pair-wise array combinations within a sample.
False-change rates of CodeLink and GeneChip were calculated from each pair-wise array comparison between arrays processed with the same starting material.
