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We have a finite number of samples which needs to define a sample covariance matrix instead of a statistical covariance matrix, expressing X(n−i) as X i covariance matrix will be mathbf{R}(N =frac{1}{N}textsc{x}_{i}textsc{x}_{i} textsc{x}_{i}^{T}.
These selection steps allowed us to define a sample of 627 individuals.
Again, these selection steps enabled us to define a sample of 576 individuals from the AIBL cohort.
The cutoff for ER or PR to define a sample as negative was 3%.
Single positive assay result from the triplicate reactions was used to define a sample as PCR positive [ 16 – 18 ].
The consecutive points define a sample path from the left to the right of the sequential plan.
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In order to select a sample in a finite population of N units with given inclusion probabilities, it is possible to define a sampling design on at most N samples that have a positive probability of being selected.
We define a sampling of a Γ-design B="(V,B) into a Γ′-design B′="(V,B′) as a surjective map ξ:B→B′ mapping each block of B into one of its subgraphs.
Define a sampling mask, S, acting on the Fourier transform of the image, F u, v), S ( u, ν ) = 1 F ( u, ν ) sampled 0 F ( u, ν ) not sampled (12).
In order to compute (B_{10}) for the hypotheses of interest of this work, it is necessary to define a sampling model for the confusion matrix (varvec{Z}) under each hypothesis.
We define a sampling probability that considers both rank and magnitude of d.
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