A computational validation for nonparametric assessment of spatial trends
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A computational validation for nonparametric assessment of spatial trendsAutor(es)
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2021Cita bibliográfica
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
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https://doi.org/10.1007/s00180-021-01108-0
Resumo
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.
Palabras chave
Parametric spatial trends
Bootstrap algorithm
Nonparametric fit
Goodness-of-fit test
Bias correction
Bootstrap algorithm
Nonparametric fit
Goodness-of-fit test
Bias correction
Descrición
Versión final aceptada de: https://doi.org/10.1007/s00180-021-01108-0 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
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