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This shows that the approximate part of our shrinkage formula is also good, and that when the inner step in [11, 12] is chosen to be 5, the numerical experiments are convergent although they did not find a convergence control sequence.
Our shrinkage statistics was defined in Eq. (2) in analogy with previous approaches [ 44- 46].
Note that our shrinkage implies that this estimate is intrinsically corrected for selection bias [ 10].
Our shrinkage LLSimpute associates the LLSimpute method with the shrinkage estimator to impute the missing values.
Finally, we further compare the performances of our shrinkage regressioni-based methods and three existing non-regression-based methods.
We have shown that our shrinkage regression-based methods perform better than the existing regression-based methods.
Similar(49)
For most of the patch-labeling injections in our database, shrinkage was not measured or reported, so we could not correct for this in our analysis.
In Section 2, we deduce our explicit shrinkage formulas in detail for the OGS problems (3), (4), and (5).
We only compare the results of our explicit shrinkage formulas with the most recent MM iteration method proposed in [10] as a simple example.
Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.
Numerical results are given to show the effectiveness of our new explicit shrinkage formulas.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com