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.
Esteban, E., S. Saldaña, and D. Salgado. 2017h. SelEditErrorMoment: Compute the conditional measurement error moments under the optimization approach to selective editing. Available at https://github.com/david-salgado/SelEditErrorMoment. R package version 0.0.1.
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