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It addresses the missing value problem as well.
The Euclidean distance is averaged to provide a simple estimation given the existence of missing correlations (due to the missing value problem).
We used a form of sensitivity analysis based on the "missing value problem".
Therefore, we modelled the pooled sequencing data as a "missing value" problem and designed an EM algorithm to estimate the minor allele frequency and sequencing error rate.
To find an optimal method to solve the missing value problem efficiently, we conducted a comprehensive performance comparison of various missing value imputation algorithms in this work.
We reduced the data to 2390 patients in order to avoid a missing value problem.
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Because these approaches are either time-consuming or leading to serious estimation errors, more advanced missing value imputation methods are needed to solve the missing value problems.
Besides, to assist experiment practioners in solving missing value problems directly before data analysis, we developed a web-based imputation tool.
In addition, the ready availability of the freeware R package makes missForest and its simulations a very convenient solution for any practical missing value problems.
The five-item model showed virtually the same level of construct validity (as demonstrated by the confirmatory factor models), and was free of the missing value problems resulting from the children's health item.
The strength of this is that the missForest method consistently produced the lowest imputation error and had the smallest prediction difference when the models used imputed laboratory values and that it is a readily available freeware R package, making it a very convenient solution for any practical missing value problems.
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