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Instead of a general measure of information transfer, the information capacity of a Gaussian channel (later referred to as information capacity) is the most often used estimator for analyzing information processing.
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The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size.
The Cramér-Rao lower bound has been proven to be a valuable tool for determining the minimal achievable measurement uncertainty and for analyzing the performance of estimators in terms of efficiency.
A suitable recovery-based error estimator is analyzed to guide the adaptive discretization.
The proposed diffuse sound PSD estimator is analyzed and compared to existing estimators.
Also in [4, 5] a maximum likelihood estimator is analyzed.
To estimate H at each voxel, we computed the PSD via Hanning-windowed Welch estimator, analyzed in time windows of the same length as those used in DFA (see Selection of Scaling Ranges for fMRI Data below).
The convergence of these estimators is analyzed.
The three estimators are analyzed and compared.
This paper proposes and analyzes an a posteriori error estimator for the finite element multi-scale discretization approximation of the Steklov eigenvalue problem.
In the following, we present and analyze the performances of the estimator for the covariance of two distances.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com