As global warming progresses, it is increasingly important to monitor and analyse spatio-temporal patterns of heat waves and other extreme climate-related events that impact urban areas. In this work, we present a novel dynamic spatio-temporal model by combining a state space model (SSM) and a generalised hyperbolic distribution to flexibly describe a spatial-temporal profile of the tail...
This study aggregates/combines global and local sub-models to build a fast and flexible spatially varying coefficient model. An approach inspired by the generalized product-of-experts method is used to aggregate the sub-models. The aggregated model has the following properties: (i) computationally efficient; (ii) the marginal likelihood is available in closed-form; (iii) each sub-model can be...
We propose a method to construct a joint statistical model for mixed-domain data to analyze their dependence. The model is characterized by two orthogonal parameters: the dependence parameter and the marginal parameter. To estimate the dependence parameter, a conditional inference together with a sampling procedure is proposed and is shown to provide a consistent estimator. Illustrative...
In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to assess how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners need to select subsets of the total available assets...
Earthquakes refer to sudden and spontaneous rupture, or slip, of a geologic fault. Although the physics of fault slips are not fully understood, the governing equations of fault slips are used along with data assimilation methods to make forecast of future fault slip behavior. On the other hand, by focusing on the location and timing of occurrences, earthquakes have also been regarded and...
Since dense geodetic and seismic networks reveal the presence of slow earthquakes and the close relationship between regular and slow earthquakes, many studies have focused on the detection of slow earthquakes and their source characterization. Global Navigation Satellite System (GNSS) continuously monitors ground deformation and is one of the most common tools used to detect slow slip events...
Pyroclastic density currents (PDCs) are hot mixtures of gas and particles generated by volcanic eruptions. They propagate on the ground at high velocity and can travel distances that are commonly of several tens of kilometers. Understanding the factors that control the long runout distance of PDCs is important for hazard assessment. In this context, we collected data on PDCs in more than 200...
Natural risks are characterized primarily by their uncontrollability, or at least, their difficulty in being controlled. In this instance, the goal is to try to understand the mechanism that causes these hazards as well as the variables that affect the processes' evolutionary behavior. In this talk, we provide a data-driven approach for identifying an evolutionary system's hidden control...
In standard regression models, pairs of covariates and response variables are observed. In the more complex case of shuffled regression (on anonymized data), we only observe a sample of covariates on the one hand, and a sample of responses on the other, but we don't know which response corresponds to each covariate. In the even more complex case where responses and covariates are not...
The worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward...
