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Inspired by real-life problems and applications, where data is incomplete and corrupted, we here extended the iterative thresholding and K residual means (ITKrM) algorithm for dictionary learning to learning dictionaries from incomplete/masked data (ITKrMM).
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The analysis used statistical methods to estimate the distribution of accidents where data was incomplete.
Finally, we present theoretical backgrounds for these covering-based rough membership functions, and illustrate how to apply them on numerically characterizing decisions under the circumstances where data are incomplete.
Mathematical modelling was used where data was incomplete.
But in cases where data are incomplete and variables are mutually related, its application is restricted.
Cases where data were incomplete were also excluded, and 2,919 patients were finally analyzed.
Where data were incomplete, the principal investigator of the primary study was contacted for further information and clarification.
Population-based studies, particularly those analysing records where data are incomplete, benefit from multiple imputation by permitting a fuller analysis of incomplete records [ 1].
Where data were incomplete we used multiple imputation via chained equations (ice) 17too complete missing data using STATA V.11 (StataCorp 2009, Texas, USA).
Patients whose symptoms did not fit the criteria for KD, had an acute fever for <5 days, or in cases where data were incomplete were excluded.
For example, Nur et al. [ 1] used imputation techniques to refine mortality estimates by including stage, morphology and grade data for colorectal cancer patients from an additional 45%% of the patient cohort where data were incomplete.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com