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#Clipping detector pdf
Google Patents Clipping detection device and methodĭownload PDF Info Publication number US20100030555A1 US20100030555A1 US12/470,233 US47023309A US2010030555A1 US 20100030555 A1 US20100030555 A1 US 20100030555A1 US 47023309 A US47023309 A US 47023309A US 2010030555 A1 US2010030555 A1 US 2010030555A1 Authority US United States Prior art keywords distribution amplitude deflection degree clipping basis Prior art date Legal status (The legal status is an assumption and is not a legal conclusion. Google Patents US20100030555A1 - Clipping detection device and method the pull-clipping is a performant procedure to detect outliers.US20100030555A1 - Clipping detection device and method.the usage of the significantly sub-efficient median estimator is overdone when samples are only rarely affected by outliers,.The results that were obtained here are probably slightly dependant of the details of the numerical experiment, notably the distribution adopted for the outliers (fraction, significance, impact on the measurement variance), however, the general conlusion would hold: it is less impacted by multiple outliers, given the very good performance of the pull-clipping process (as measured by a Matthews Correlation Coefficient of 99% in our experiment).it is always statistically more efficient than the median, its variance being 30 to 40% smaller than the one of the median in absence of outliers (which is expected to be the vast majority of the cases, 81% in our experiment), and still 10% smaller in presence of a single outlier.the inverse-variance weighted mean on pull-clipped sample.Īmong these 3 estimators, the inverse-variance weighted mean on pull-clipped sample appears to have the best performances:.the inverse-variance weighted mean on (weighted) \(\sigma\)-clipped sample,.We tested three different approachs to compute the mean of a small-size ( \(n=4\)) sample in presence of a small fraction (5% probability) of outlying (10 \(\sigma\)-level) values:
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\sigma_m &= \left(\eta_n \langle 1/\sigma_i \rangle_i^2\right)^(0,1)\) distribution (except for the outlying fraction), from which one can conclude that all three sample mean estimators are not significantly biased, and mean errors are well estimated.
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