Robust estimation in very small samples
Computational Statistics & Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications
Computational Statistics & Data Analysis
Output distributional influence function
IEEE Transactions on Signal Processing
Robust Estimation of Bioaffinity Assay Fluorescence Signals
IEEE Transactions on Information Technology in Biomedicine
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We evaluated standard robust methods in the estimation of fluorescence signal in novel assays used for determining the biomolecule concentrations. The objective was to obtain an accurate and reliable estimate using as few observations as possible by decreasing the influence of outliers. We assumed the true signals to have Gaussian distribution, while no assumptions about the outliers were made. The experimental results showed that arithmetic mean performs poorly even with the modest deviations. Further, the robust methods, especially the M-estimators, performed extremely well. The results proved that the use of robust methods is advantageous in the estimation problems where noise and deviations are significant, such as in biological and medical applications.