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According to Weber, this type of coordination would be inefficient, especially because it would be incapable of solving the problem of imputation (i.e. of accurately determining the relative values of capital goods).
To tackle the problem of imputation, AlphaImpute combines long-range phasing, haplotype library imputation, simple phasing and imputation rules, and segregation analysis.
Furthermore, the portion of the genome represented by multiple sequencing reads is also high, making the problem of imputation of missing genotypes small relative to human studies [ 45].
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In this work, we consider the problems of imputation and joint-testing from summary statistics and therefore selected the MSE of imputed z-scores at observed SNPs and MSE of computed joint-test statistics as our objective functions, respectively (see Sections 2.3 and 2.4).
For example, in this work, we consider the problems of imputation and joint-testing from summary statistics, using imputation error and joint-test accuracy as the objective function, respectively.
Recent years have seen the problem of congeniality of the imputation model and substantive model earning greater attention.
We attend to the problem of missing data by using multiple imputation with SAS 9.2 PROC MI and MIANALYZE [ 33]; see also recommendations by Mayer et al. [ 34].
It is not known whether imputation methods can robustly address the problem of missing data, and whether predictive models will remain accurate when imputed values are used.
It is not known whether modern imputation methods can robustly address the problem of missing data, and whether predictive models will remain accurate when imputed values are used.
To address the problem of missing data, we will run multiple imputations using the R-package Amelia II (The R Foundation for Statistical Computing, Vienna, Austria) which performs expectation-maximization with bootstrapping algorithms [ 63– 63].
The problem of missing data was addressed with the multiple imputation procedure.
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