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Unlike other denoising methods, missing data recognition does not match the whole data with the acoustic models, but instead considers part of the signal as missing, i.e. corrupted by noise.
It was recommended that the risks linked to inadequate forecasting methods, missing data or external project variables (complex and uncertain economic growth forecasts, variations in oil prices having an effect on traffic levels) be better analysed.
Furthermore, as recommended, the statistical methods, missing data reporting, and use of sensitivity analyses were judged.
Two independent reviewers assessed the methodological quality of included studies, examining selection methods, missing data, loss-to-follow-up, misclassification or measurement errors of exposures or outcomes, and confounding bias.
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The results remained unchanged after using two methods of missing data replacement, the maximum bias method, and the mean value of the group.
If necessary, multiple imputation or Bayesian methods for missing data will be used as appropriate to address any missing data.
Most statistical methods for missing data require some assumptions on missingness mechanisms.
Many existing statistical methods for missing data require observations at individual levels.
Generally speaking, there are two categories of methods for missing data problems: guaranteed cost ones and data imputation based ones.
We attempted to overcome this by analysing the data using different imputation methods for missing data.
Intention to treat analysis used multiple imputation methods for missing data.
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