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To evaluate possible finite sample size distortions, we generate 10,000 samples of length 200 of the following model begin{aligned} left.
If the distribution of the data is (approximately) known, finite sample size eigenvalue distributions have been determined for several data distributions.
Because the available samples with ground truth are often limited in medical imaging research, the finite sample size is a limiting factor in the development of CAD systems.
In fact, finite sample size distortion has now come to be considered as one of several possible explanations behind the forward premium puzzle.
In this talk, we will review some of the issues associated with classifier design and validation under the constraint of finite sample size.
To evaluate possible finite sample size distortions, we generate 10,000 samples of length 200 (equivalent to 50 years of quarterly data) of the following model begin{aligned} left.
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Practical implementations for obtaining designs with finite sample sizes are demonstrated.
The finite sample sizes for the four tests are displayed in the first panel of Table 1.
We evaluate the FIM in censored samples for finite sample sizes and determine its limiting form as the sample size increases.
However, because finite sample sizes are used in practice, we investigated a small sample power of the original and modified Wilcoxon rank-sum tests.
For finite sample sizes, the mean, median, standard error (se) and 95% confidence intervals of the estimator are calculated based on 500 independent runs.
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