Flexible spatiotemporal models for environmental processes

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Environmental statistics methodology is concerned with diverse global measurements of natural phenomena. Some examples of problems which can be of interest in this field are: the modeling of mean concentrations of pollutants, the study of relationships between airborne pollution and lung diseases, and spatiotemporal maximum temperature trends over a region. Environmental processes commonly involve observations made at fixed locations across different instants in time. My research programme focuses on geostatistical processes, i.e. uni- and multivariate processes observed at locations that vary continuously across a region of interest. The main interest in geostatistics lies in predicting the process of interest at future times (temporal prediction) and at unobserved locations in space (spatial interpolation), while accounting for complex correlation structures.