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Verónica Crespo-Pereira es licenciada en publicidad y relaciones públicas por la Universidad de Vigo y máster en producción y gestión audiovisual por la Universidad de A Coruña. Su actividad profesional se ha desarrollado en el sector audiovisual en los departamentos de producción y dirección para series de ficción y programas. Trabaja como investigadora en la Universidad de Vigo donde lleva a cabo su tesis doctoral.

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Variogram estimation is a major issue for statistical inference of spatially correlated random variables. Most natural empirical estimators of the variogram cannot be used for this purpose, as they do not achieve the conditional negative-definite property. Typically, this problem's resolution is split into three stages: empirical variogram estimation; valid model selection; and model fitting. To accomplish these tasks, there are several different approaches strongly defended by their authors. Our work's main purpose was to identify these approaches and compare them based on a numerical study, covering different kind of spatial dependence situations. The comparisons are based on the integrated squared errors of the resulting valid estimators. Additionally, we propose an easily implementable empirical method to compare the main features of the estimated variogram function.

In this work, the nonparametric kernel prediction will be considered for stochastic processes, when a random design is assumed for the spatial locations. We will check that, under rather general conditions, the mean-squared prediction error tends to be negligible, as the sample size increases. However, the use of the optimal bandwidth demands the estimation of unknown quantities, whose approximation in an accurate way often turns out to be difficult in practice. Hence, alternative cross-validation approaches will be provided for the selection of both local and global bandwidths. Numerical studies were carried out in order to analyse the performance of the nonparametric predictor for both simulated and real data.

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO 2 levels, by using geostatistical approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This methodology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Asymptotic normality, Intrinsic stationarity, Isotropy, Kernel, Random process, Variogram, 62G05, 62G10,