2020/02/13 by Andrea Meilán-Vila, Meilán-Vila, Andrea, Rubén Fernández‐Casal +3 · 1 citation
Economics, Econometrics and Finance · Environmental Science · #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis
paper · pdf · doi:10.48550/arxiv.2002.05489
openalex publication_date 2020/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The analysis of continuously spatially varying processes usually considers\ntwo sources of variation, namely, the large-scale variation collected by the\ntrend of the process, and the small-scale variation. Parametric trend models on\nlatitude and longitude are easy to fit and to interpret. However, the use of\nsimple parametric models for characterizing spatially varying processes may\nlead to misspecification problems if the model is not appropriate. Recently,\nMeil 'an-Vila et al. (2019) proposed a goodness-of-fit test based on an\nL2-distance for assessing a parametric trend model with correlated errors,\nunder random design, comparing a parametric and a nonparametric trend\nestimators. The present work aims to provide a detailed computational analysis\nof the behavior of this approach using different bootstrap algorithms for\ncalibration, under a fixed-design geostatistical framework. Asymptotic results\nfor the test are provided and an extensive simulation study, considering\ncomplexities that usually arise in geostatistics, is carried out to illustrate\nthe performance of the proposal.\n