Editing systematic unity measure errors through mixture modelling - ARCHIVED

In Official Statistics, data editing process plays an important role in terms of timeliness, data accuracy, and survey costs. Techniques introduced to identify and eliminate errors from data are essentially required to consider all of these aspects simultaneously. Among others, a frequent and pervasive systematic error appearing in surveys collecting numerical data, is the unity measure error. It highly affects timeliness, data accuracy and costs of the editing and imputation phase. In this paper we propose a probabilistic formalisation of the problem based on finite mixture models. This setting allows us to deal with the problem in a multivariate context, and provides also a number of useful diagnostics for prioritising cases to be more deeply investigated through a clerical review. Prioritising units is important in order to increase data accuracy while avoiding waste of time due to the follow up of non-really critical units.