A computational validation for nonparametric assessment of spatial trends

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 simple parametric models for characterizing spatially varying processes may lead to misspecification problems if the model is not appropriate. Recently, Meilán-Vila et al. ( 2019) proposed a goodness-offit test based on an L 2 -distance for assessing a parametric trend model with correlated errors, under random design, comparing a parametric and a nonparametric trend estimators. The present work aims to provide a detailed computational analysis of the behavior of this approach using different bootstrap algorithms for calibration, 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.

Asymptotic properties of nonparametric quantile estimation with spatial dependency

“…Rachdi, Laksaci and Al-Awadhi (2021) proposed conditional quantile estimate by using parametric and nonparametric approach for spatiofunctional data. We can also refer to others nonparametric context as in Weller and Hoeting (2020), Xu and Bai (2020), Gupta andHidalgo (2020), Meilán-Vila, Fernández-Casal andFrancisco-Fernández (2020), Kurisu (2020), Fuentes-Santos, González-Manteiga andZubelli (2020).…”