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Open access

Ignacio Arbue´s, Pedro Revilla and David Salgado

Abstract

We set out two generic principles for selective editing, namely the minimization of interactive editing resources and data quality assurance. These principles are translated into a generic optimization problem with two versions. On the one hand, if no cross-sectional information is used in the selection of units, we derive a stochastic optimization problem. On the other hand, if that information is used, we arrive at a combinatorial optimization problem. These problems are substantiated by constructing a so-called observation-prediction model, that is, a multivariate statistical model for the nonsampling measurement errors assisted by an auxiliary model to make predictions. The restrictions of these problems basically set upper bounds upon the modelled measurement errors entering the survey estimators. The bounds are chosen by subject-matter knowledge. Furthermore, we propose a selection efficiency measure to assess any selective editing technique and make a comparison between this approach and some score functions. Special attention is paid to the relationship of this approach with the editing fieldwork conditions, arising issues such as the selection versus the prioritization of units and the connection between the selective and macro editing techniques. This approach neatly links the selection and prioritization of sampling units for editing (micro approach) with considerations upon the survey estimators themselves (macro approach).

Open access

David Salgado, M. Elisa Esteban, Maria Novás, Soledad Saldaña and Luis Sanguiao

Abstract

We propose to use the principles of functional modularity to cope with the essential complexity of statistical production processes. Moving up in the direction of international statistical production standards (GSBPM and GSIM), data organisation and process design under a combination of object-oriented and functional computing paradigms are proposed. The former comprises a standardised key-value pair abstract data model where keys are constructed by means of the structural statistical metadata of the production system. The latter makes extensive use of the principles of functional modularity (modularity, data abstraction, hierarchy, and layering) to design production steps. We provide a proof of concept focusing on an optimisation approach to selective editing applied to real survey data in standard production conditions at the Spanish National Statistics Institute. Several R packages have been prototyped implementing these ideas. We also share diverse aspects arising from the practicalities of the implementation.