Comparison of the performance of multiple imputation models in filling gaps in hourly and daily meteorological series from two locations in the state of São Paulo-Brazil-Reference-Cited by-同舟云学术

Comparison of the performance of multiple imputation models in filling gaps in hourly and daily meteorological series from two locations in the state of São Paulo-Brazil

Published:2023-09-30 Issue: Volume: Page:
ISSN:2363-6203
Container-title:Modeling Earth Systems and Environment
language:en
Short-container-title:Model. Earth Syst. Environ.

Author:

Maziero Luana Possari^ORCID,Rodrigues Sérgio Augusto^ORCID,Pai Alexandre Dal^ORCID,Cremasco Camila Pires^ORCID,Gabriel Filho Luís Roberto Almeida^ORCID

Funder

Coordination for the Improvement of Higher Education Personnel

Publisher

Springer Science and Business Media LLC

Subject

Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s40808-023-01863-7.pdf

Reference37 articles.

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2. Aleryani A, Wang W, de la Iglesia B (2020) Multiple imputation ensembles (MIE) for dealing with missing data. SN Comput Sci 1:1–20. https://doi.org/10.1007/S42979-020-00131-0/FIGURES/9

3. Alves LER, Gomes HB (2020) Validação da Imputação Múltipla via Predictive Mean matching para Preenchimento de Falhas nos Dados Pluviométricos da Bacia do Médio São Francisco. Anuário do Instituto de Geociências 43:199–206. https://doi.org/10.11137/2020_1_199_206

4. Andrade DF, Borgatto AF, Araujo PH, Schmitt J (2019) Técnicas de imputação de dados na análise de questionários contextuais. Caderno de Pesquisa, 1st edn. Cebraspe, Brasília, pp 13–107

5. Armina R, Mohd Zain A, Ali NA, Sallehuddin R (2017) A review on missing value estimation using imputation algorithm. J Phys Conf Ser 892:012004. https://doi.org/10.1088/1742-6596/892/1/012004