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Spatial and Temporal Data Analysis

In this field, researchers are focused on tackling challenges related to understanding complex problems that involve both spatial and temporal variations. These issues require advanced techniques to model the simultaneous impact of these variations effectively.

The data involved in these studies are often extensive, making traditional analytical methods impractical. Instead, researchers need to create and utilize efficient computational methods to handle the intricacies of the data. The complexity of multidimensional interactions limits the applicability of the classical methods based on the Gaussian paradigm. Therefore, the new methods are developed that involve Laplace distributed errors. Our team's expertise lies in exploring non-Gaussian models, specifically those driven by generalized Laplace errors. By delving into these theoretical studies, we can pioneer innovative statistical methodologies, enhancing our understanding and application of these complex spatial and temporal variations.