Flexible spatiotemporal models for environmental processes

环境统计方法涉及对自然现象的各种全球计量。在这一领域可能感兴趣的问题的一些例子是：污染物的平均浓度的建模，空气污染和肺部疾病之间的关系的研究，和时空最高温度的趋势在一个区域。环境过程通常涉及在不同时刻在固定地点进行的观测。我的研究计划侧重于地质统计过程，即在感兴趣的区域连续变化的位置观察到的单变量和多变量过程。地质统计学的主要兴趣在于预测未来时间（时间预测）和空间中未观察到的位置（空间插值）的过程，同时考虑复杂的相关结构。

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.