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Statistical inference of unknown attribute values in databases

In this paper, we propose to use statistical methods to estimate unknown attribute values in databases, as compared to assigning possible values at users’ discretion in common practice. Regression models and classification analysis are introduced for estimating continuous and categorical unknown attribute values, respectively. Procedures for selecting relevant attributes in a relation and preliminary experimental results of the proposed models on a real life database are also presented.