most citedA goodness-of-fit test for regression models with spatially correlated errors

8 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME20241 cited

Nonparametric conditional risk mapping under heteroscedasticity

Rubén Fernández-Casal, Sergio Castillo-Páez, Mario Francisco-Fernández

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of…

stat.ME20248 cited

A goodness-of-fit test for regression models with spatially correlated errors

Andrea Meilán-Vila, Jean D. Opsomer, Mario Francisco-Fernández +1

The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a -d…

stat.ME20248 cited

Nonparametric geostatistical risk mapping

Rubén Fernández-casal, Sergio Castillo-Páez, Mario Francisco-Fernández

In this work, a fully nonparametric geostatistical approach to estimate threshold exceeding probabilities is proposed. To estimate the large-scale variability (spatial trend) of th…

stat.ME20246 cited

Nonparametric estimation of circular trend surfaces with application to wave directions

Andrea Meilán-Vila, Rosa M. Crujeiras, Mario Francisco-Fernández

In oceanography, modeling wave fields requires the use of statistical tools capable of handling the circular nature of the {data measurements}. An important issue in ocean wave ana…

stat.ME2020

A computational validation for nonparametric assessment of spatial trends

Andrea Meilán-Vila, Rubén Fernández-Casal, Rosa M. Crujeiras Mario Francisco-Fernández

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 th…