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In their study, 5% of observed genotypes were randomly set to be missing, various imputation methods were used to impute the "missing" genotypes, and the imputation error rates for the various methods were then compared.
Dealing with missing data will include various imputation methods.
In the second subsection, we describe how we measure the performance of various imputation methods.
Various imputation methods are available, some of which are reviewed in [ 12].
Then we investigated whether the microarray data from different species would affect the performance of various imputation methods or not.
Various imputation methods have been suggested to fill in missing values in order to receive complete data sets.
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Several imputation methods using various statistical models such as the haplotype-clustering algorithm [12], the hidden Markov model (HMM) [5], and the Markov Chain model [13], have been proposed.
Several imputation methods based on various statistical models, such as the haplotype clustering algorithm (Scheet and Stephens, 2006), the hidden Markov model (HMM; Browning and Browning, 2007), the Expectation Maximization (EM) algorithm (Qin et al., 2002; Scheet and Stephens, 2006) and the Markov Chain model (Li et al., 2010), have been proposed.
Several imputation methods should be avoided, in particular PMS imputation.
Findings were robust across various model specifications and imputation methods used.
JO and GW suggested several improvements to the imputation methods.
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