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A nonparametric procedure to estimate the conditional probability that a nonstationary 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 variability. 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 variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. 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 of precipitations in the USA.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

The objective is to provide a nonparametric bootstrap method for functional data that consists of independent realizations of a continuous one-dimensional process. The process is assumed to be nonstationary, with a functional mean and a functional variance, and dependent. The resampling method is based on nonparametric estimates of the model components. Numerical studies were conducted to check the performance of the proposed procedure, by approximating the bias and the standard error of two estimators. A practical application of the proposed approach to pollution data has also been included. Specifically, it is employed to make inference about the annual trend of ground-level ozone concentration at Yarner Wood monitoring station in the United Kingdom. Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

The current study aims to provide nonparametric estimators of the conditional variance and the dependence structure of a heteroscedastic spatial process. When assuming zero mean along the domain, the approximation of the variance can be addressed by linear smoothing of the squared observations. Then, the variogram can be estimated from the standardized data. In the presence of a non-zero deterministic trend, we suggest a modification of the latter method that involves the residuals obtained from a local linear estimation of the trend, together with corrections of the biases derived from the use of these residuals. This work includes the results of numerical studies carried out to check the performance of the proposed approach. In addition, the proposed methodology is applied to monthly precipitation data collected on the continental part of USA. [ABSTRACT FROM AUTHOR]

The current work provides a nonparametric resampling procedure for approximating the (unconditional) probability that a spatial variable surpasses a prefixed threshold value. The existing approaches for the latter issue require assuming constant variance throughout the observation region, thus our proposal has been designed to be valid under heteroscedasticity of the spatial process. To develop the new methodology, nonparametric estimates of the variance and the semivariogram functions are computed by using bias-corrected residuals, which are then employed to derive bootstrap replicates for approximating the aforementioned risk. The performance of this mechanism is checked through numerical studies with simulated data, where a comparison with a semiparametric method is also included. In addition, the practical application of this approach is exemplified by estimating the risk of rainwater accumulation in the United States, during a specific period. [ABSTRACT FROM AUTHOR]