Although K-R DDF approximation is recommended to maintain the type I error rate, its small sample performance was evaluated mainly on normal-distributed outcomes under repeated measures designs [ 14, 15].
For these reasons, the estimates of out-of-sample performance are likely to be generalizable to other current settings.
The effect of cohesion on sampler performance is explored.
For the investment to employee and government, non-connected samples' performance is better than connected samples'.
As shown in Fig. 8, compared with the non-AFH results, the WiFi RSSI sampling performance is improved in the AFH-enabled test case such as AP 1 2, 3, and 6, where the RSSI values are higher than −80 dBm.
Besides, difference of the three samplers on the scope of the target analytes and exposure time, as well as the effects of environmental factors, e.g. hydrodynamic conditions, temperature, pH, ionic strength, DOM, on sampling performance were also introduced.
For the detection of segmental gains and losses of one or more copies in 35 NB samples, performance was very high even when measured in a numerical background (Table 1).
We calculated standardized error as a metric for evaluating the performance of the prediction models as follows: (1) (2) (Estd, standardized error; Eave, averaged error; yp, prediction value; yt, teacher signal value; N, sample number; V, variance of all samples) Performance is considered to be improved with a decrease in the standardized error.
(13) S e n s i t i v i t y = T P T P + F N (14) S p e c i f i c i t y = T N T N + F P Due to the limited number of samples, performance is evaluated using 10 fold cross-validation.
The experimental results that can be found in Table 8 show that even with half the samples, the performances are directly comparable to those achieved when extracting 600 samples for the respective descriptors and codebook.
