Sentence examples for missing data estimation from inspiring English sources

The MC framework is provided by Genie, with imputation of missing data, estimation of population haplotype frequencies and maximum likelihood estimates (MLE) of individuals' haplotype pairs provided by the hapMC component.

In this paper, an accurate missing data estimation method is proposed by combining Bayesian robust matrix factorization (BRMF) and particle swarm optimization (PSO).

This section formulates the missing data estimation procedure for (22) based on the automated (summarized) information at categorical levels defined by relevant covariates of the model.

The SF-36 records were scored with the QualityMetric's missing data estimation (MDE).

The reporting of statistical power, sample size, normalisation and missing data estimation techniques requires a more rigorous treatment.

There was a substantial missing growth data at some follow-up periods, although missing data estimation models were used to minimise bias.

Filtering and missing data estimations were performed independently for PV_Sep and Me_Sep data sets, and jointly for PV_Joint and Me_Joint (i.e., considering the joint matrix including 124 PV genotypes plus 154 Me genotypes).

In a mixed model analysis with missing data, estimations of future observations are made on the basis of observations made earlier in the trial, thereby reflecting a divergence more accurately but less conservatively.

These analyses were done in MPlus 5.2 software using maximum likelihood estimation to account for missing data and estimation of parameters for trajectories.

Approaches of missing data mask estimation, such as Bayesian classification [17], harmonic mask estimation [10], local SNR-based mask estimation [18, 19], and VQ mask estimation [9] mainly exploit characteristics of the speech signal.

For application in PopPK analyses performed using nonlinear mixed-effects modeling with NONMEM, we identified five methods for handling missing categorical data, which are presented in Table  I: discarding subjects with missing data (DROP), estimation of an additional covariate effect for the missing group (EXTRA), and several methods using mixture models (MIX).