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A computational validation for nonparametric assessment of spatial trends
dc.contributor.author | Meilán-Vila, Andrea | |
dc.contributor.author | Fernández-Casal, Rubén | |
dc.contributor.author | Crujeiras-Casais, Rosa M. | |
dc.contributor.author | Francisco-Fernández, Mario | |
dc.date.accessioned | 2023-11-24T18:07:58Z | |
dc.date.available | 2023-11-24T18:07:58Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Meilán-Vila, A., Fernández-Casal, R., Crujeiras, R.M. et al. A computational validation for nonparametric assessment of spatial trends. Comput Stat 36, 2939–2965 (2021). https://doi.org/10.1007/s00180-021-01108-0 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34332 | |
dc.description | Versión final aceptada de: https://doi.org/10.1007/s00180-021-01108-0 | es_ES |
dc.description | This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00180-021-01108-0 | es_ES |
dc.description.abstract | The analysis of continuously spatially varying processes usually considers two sources of variation, namely, the large-scale variation collected by the trend of the process, and the small-scale variation. Parametric trend models on latitude and longitude are easy to fit and to interpret. However, the use of parametric models for characterizing spatially varying processes may lead to misspecification problems if the model is not appropriate. Recently, Meilán-Vila et al. (TEST 29:728–749, 2020) proposed a goodness-of-fit test based on an -distance for assessing a parametric trend model with correlated errors, under random design, comparing parametric and nonparametric trend estimates. The present work aims to provide a detailed computational analysis of the behavior of this approach using different bootstrap algorithms for calibration, one of them including a procedure that corrects the bias introduced by the direct use of the residuals in the variogram estimation, under a fixed design geostatistical framework. Asymptotic results for the test are provided and an extensive simulation study, considering complexities that usually arise in geostatistics, is carried out to illustrate the performance of the proposal. Specifically, we analyze the impact of the sample size, the spatial dependence range and the nugget effect on the empirical calibration and power of the test. | es_ES |
dc.description.sponsorship | The authors acknowledge the support from the Xunta de Galicia grant ED481A- 2017/361 and the European Union (European Social Fund - ESF). This research has been partially supported by MINECO grants MTM2016-76969-P and MTM2017-82724-R, and by the Xunta de Galicia (Grupo de Referencia Competitiva ED431C-2016-015, ED431C-2017-38 and ED431C-2020-14, and Centro de Investigación del SUG ED431G 2019/01), all of them through the ERDF. The authors also thank two anonymous referees and the Associate Editor for their comments that significantly improved this article. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A- 2017/361 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2017-38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-76969-P/ES/MODELIZACION NO PARAMETRICA DE DINAMICAS Y DEPENDENCIAS EN SISTEMAS COMPLEJOS | 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 | es_ES |
dc.relation.isversionof | https://doi.org/10.1007/s00180-021-01108-0 | |
dc.relation.uri | https://doi.org/10.1007/s00180-021-01108-0 | es_ES |
dc.rights | Todos os dereitos reservados. All rights reserved. | es_ES |
dc.subject | Parametric spatial trends | es_ES |
dc.subject | Bootstrap algorithm | es_ES |
dc.subject | Nonparametric fit | es_ES |
dc.subject | Goodness-of-fit test | es_ES |
dc.subject | Bias correction | es_ES |
dc.title | A computational validation for nonparametric assessment of spatial trends | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.1007/s00180-021-01108-0 |
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