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Big-But-Biased Data Analytics for Air Quality
dc.contributor.author | Borrajo, Laura | |
dc.contributor.author | Cao, Ricardo | |
dc.date.accessioned | 2020-11-23T18:24:07Z | |
dc.date.available | 2020-11-23T18:24:07Z | |
dc.date.issued | 2020-09-22 | |
dc.identifier.citation | Borrajo, L.; Cao, R. Big-But-Biased Data Analytics for Air Quality. Electronics 2020, 9, 1551. https://doi.org/10.3390/electronics9091551 | es_ES |
dc.identifier.issn | 2079-9292 | |
dc.identifier.uri | http://hdl.handle.net/2183/26744 | |
dc.description.abstract | [Abstract] Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained. | es_ES |
dc.description.sponsorship | This research has been supported by MINECO Grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015), both of them through the ERDF; and by CITIC, a Research Centre of the Galician University System financed by the Consellería de Education, Universidades y Formación Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaría Xeral de Universidades (Ref. ED431G 2019/01) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016/015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | |
dc.relation.uri | https://doi.org/10.3390/electronics9091551 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Air quality | es_ES |
dc.subject | Automatic bandwidth selection | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Bootstrap | es_ES |
dc.subject | Kernel density estimation | es_ES |
dc.subject | Large sample size | es_ES |
dc.subject | Sampling bias | es_ES |
dc.subject | Smart city | es_ES |
dc.title | Big-But-Biased Data Analytics for Air Quality | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Electronics | es_ES |
UDC.volume | 9 | es_ES |
UDC.issue | 9 | es_ES |
UDC.startPage | 1551 | es_ES |
dc.identifier.doi | 10.3390/electronics9091551 |
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