Preprocessing alternatives for compositional data related to water, sanitation and hygiene
Fecha
2020Autor
Quispe-Coica, Alejandro
Pérez-Foguet, Agustí
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Resumen
The Sustainable Development Goals (SDGs) 6.1 and 6.2 measure the progress of urban and rural populations in
their access to different levels of water, sanitation and hygiene (WASH) services, based on multiple sources of information. Service levels add up to 100%; therefore, they are compositional data (CoDa). Despite evidence of zero
value, missing data and outliers in the sources of information, the treatment of these irregularities with different
statistical techniques has not yet been analyzed for CoDa in the WASH sector. Thus, the results may present biased estimates, and the decisions based on these results will not necessarily be appropriate. In this article, we
therefore: i) evaluate methodological imputation alternatives that address the problem of having either zero
values or missing values, or both simultaneously; and ii) propose the need to complement the point-to-point
identification of the WHO/UNICEF Joint Monitoring Program (JMP) with other robust alternatives, to deal with
outliers depending on the number of data points. These suggestions have been considered here using statistics
for CoDa with isometric log-ratio (ilr) transformation. A selection of illustrative cases is presented to compare
performance of different alternatives
Palabras clave
Outliers; Global monitoring; Robust regression; Mahalanobis distanceEnlace al recurso
https://doi.org/10.1016/j.scitotenv.2020.140519Colecciones
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