Also, we use a fractal dimension approach to characterize the error of the estimation of the recourse function based on sampling.
That is, the WSE loss function based on the sample mean is more efficient than that using the sample median when no outlier exists.
One of the important applications is to estimate an unknown density function based on random samples [1 3].
Accordingly, the weighted squared error (WSE) loss function based on the sample mean is utilized in the line-of-sight (LOS) condition, where the sensor state is determined by the classical statistical testing [19, 20].
The WSE loss function based on the sample median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the sample mean is employed when statistical testing indicates that the sensor is in the LOS state.
At first, the proposed adaptive LOS/NLOS mixture localization method uses the WSE loss function based on the sample mean when the sensor is judged by the LOS sensor and the WSE loss function using the sample median if the sensor is predicted to be the LOS/NLOS sensor.
The proposed adaptive methods utilized the loss function as the WSE in which the sample mean was used when the sensor was identified as the LOS sensor, while the WSE loss function based on the sample median was employed when the testing result that the sensor is in the LOS/NLOS state is valid.
We left out one sample at a time, then used the remaining 79 samples to select the 5 antibodies with values increased in tumor patient samples according to the p-values for 2-sample T-tests of tumor vs. non-tumor samples, and constructed the resulting discriminant function based on the 79 samples.
The four best performing peptides in the UKCTOCS cases (p53-9, p53-25, p53-25, p53-78), were selected to form part of a DA aiming to correctly classify each sample in turn using a discriminant function based on the remaining samples ('leave-one-out' cross-validation) (Supplementary data 2).
In order to address these weaknesses, we propose a bidirectional MSE cost function based on multiple adjacent samples so that the number of channel scenarios under which the differential MMSE performs beneficial adaptation is substantially increased.
