Abstract
This paper investigates the phenomenon of the incomplete data samples by analyzing their structure and also resolves the necessary procedures regularly used in missing data analysis. The research gives a crucial perceptive of the techniques and mechanisms needed in dealing with missing data issues in general. The motivation for writing this brief overview of the topic lies in the fact that statistical researchers inevitably meet missing data in their analysis. The authors examine the applicability of regular approaches for handling the missing data situations. Based on several previously published results, the authors provide an example of the incomplete data sample model that can be implemented when confronting with specific missing data patterns.
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