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Similarly, another CASA system extracts speaker homogeneous T-F regions and employs speaker models and missing data techniques to group them into speech streams [6].
To account for this, we used missing data techniques.
Owing to the low number of missing values, no missing data techniques were applied.
Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model.
Third, we were able to implement missing data techniques to minimise bias and presented both the complete cases and imputed data sets results to give more insight.
This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation.
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missing data technique.
The missing data technique (MDT) is a promising approach.
Solutions have been presented to solve this issue, such as feature-based [1] [3], score-based [4],[5], model-based [6] [8], i-vectors [9], and the missing data technique (MDT) [10] [12].
MI is a missing data technique that imputes plausible values for the missing values.
To reduce potential selection bias a missing data technique (multiple imputation using chained equations) was used to impute the missing confounder values 11 only.
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