Nonparametric approximation of conditional risk in nonstationary geostatistical processes

Autor:	Rubén Fernández-Casal, Sergio Castillo-Páez, Mario Francisco-Fernández
Rok vydání:	2022
Předmět:	Statistics::Methodology
DOI:	10.21203/rs.3.rs-1398694/v1
Popis:	A nonparametric procedure to estimate the conditional probability that a geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variogram. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variogram is approximated by a flexible bias-corrected estimator based on the residuals. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a40b18ced14ff8fdedda83fc410a14d9 https://doi.org/10.21203/rs.3.rs-1398694/v1 Zobrazit plný text záznamu