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The RMA background correction method corrects the perfect match (PM) probe intensities by using a model based on the assumption that the observed intensities are the sum of signal and noise.
Using IMU data, the proposed motion estimation method corrects the accumulated errors via least-squares fitting with constraints that are obtained from IMU measurements and from prior knowledge of the motion.
The statistical average of this method corrects the spurious shock speed, as will be proved with a scalar model problem and demonstrated by a wide range of numerical examples in inviscid detonation waves in both one and two space dimensions.
The semi-parametric method corrects the estimator for both autoregressive and moving average errors, and this implies that it is possible to determine the significance of these parameters via conventional inference as long as the regressors are I(1) except for series that are truly exogenous.
This registration method corrects the 3D OCT data for motion on a per A-scan basis.
This method corrects the bias of Method 1 and was developed by Arditi and Akçakaya [ 6].
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The method correcting the topside contribution to TEC gives the best results.
The hook-method corrects the raw intensities for the non-specific background hybridization in a sequence-specific manner, for the potential saturation of the probe-spots with bound transcripts and for the sequence-specific binding of specific transcripts.
Thus, our research has focused on methods correcting the mutation carrying transcripts.
The proposed IVX-QR methods correct the distortion arising from persistent multivariate predictors while preserving discriminatory power.
The calibration method corrects for the effect of thermal conductivity of the DSC apparatus on the magnitude and phase angle of the heat capacity.
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Justyna Jupowicz-Kozak
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