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To examine the effect of missing data and our assumptions on the model, we analyzed the data: 1) after excluding patients with missing data, and 2) imputing values for missing data based on frequencies observed for subjects with non-missing data.
Some efforts have also been dedicated to improve the prediction of missing data, based on correctly received ones (see, e.g., [14, 22] for details).
The technique employs temporal auto-correlation of fluxes, and estimate the missing data based on the mean value of available data under similar meteorological conditions within an averaging window of 7 days.
Given the complexity of the biological networks affected by the various stimuli and inhibitors, we decided to approach this challenge by imputing the missing data based solely on the exiting measured data.
In the longitudinal analyses the programme accounts for missing data based on the observed data.
In this paper, four practical, missing data based strategies for dealing with responses in the category 'not applicable' are examined in the context of item response theory.
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The authors of [20] estimate the missing data masks based on computational ASA.
This paper proposes an active resilient control strategy for singular networked control systems with external disturbances and missing data scenario based on sampled-data scheme.
Dealing with ordinal data as well as with missing data is based on the assumption that the statistical model fits the set of observed data.
Kokaram employed a variety of 2D and 3D autoregressive models to achieve interpolation for missing data, again based on a Bayesian framework [24].
This technique fills in various values for each missing data point based on a statistical model.
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lost data based
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missing frequencies based
missing measurements based
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