Browsing by Author "Xiao X"

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    A Statistical Model for Earthquake And/Or Rainfall Triggered Landslides
    (Frontiers Media S.A., 2021-02-04) Frigerio Porta G; Bebbington M; Xiao X; Jones G; Xu C
    Natural hazards can be initiated by different types of triggering events. For landslides, the triggering events are predominantly earthquakes and rainfall. However, risk analysis commonly focuses on a single mechanism, without considering possible interactions between the primary triggering events. Spatial modeling of landslide susceptibility (suppressing temporal dependence), or tailoring models to specific areas and events are not sufficient to understand the risk produced by interacting causes. More elaborate models with interactions, capable of capturing direct or indirect triggering of secondary hazards, are required. By discretising space, we create a daily-spatio-temporal hazard model to evaluate the relative and combined effects on landslide triggering due to earthquakes and rainfall. A case study on the Italian region of Emilia-Romagna is presented, which suggests these triggering effects are best modeled as additive. This paper demonstrates how point processes can be used to model the triggering influence of multiple factors in a large real dataset collected from various sources.
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    An asset subset-constrained minimax optimization framework for online portfolio selection
    (Elsevier Ltd, 2024-11-15) Yin J; Zhong A; Xiao X; Wang R; Huang JZ
    Effective online portfolio selection necessitates seamless integration of three key properties: diversity, sparsity, and risk control. However, existing algorithms often prioritize one property at the expense of the others due to inherent conflicts. To address this issue, we propose an asset subset-constrained minimax (ASCM) optimization framework, which generates optimal portfolios from diverse investment strategies represented as asset subsets. ASCM consists of: (i) a minimax optimization model that focuses on risk control by considering a set of loss functions constrained by different asset subsets; (ii) the construction of asset subsets via price-feature clipping, which effectively reduces redundant assets in the portfolio; (iii) a state-based estimation of price trends that guides all ASCM loss functions, facilitating the generation of sparse solutions. We solve the ASCM minimax model using an efficient iterative updating formula derived from the projected subgradient method. Furthermore, we achieve near O(1) time complexity through a novel initialization scheme. Experimental results demonstrate that ASCM outperforms eight representative algorithms, including the best constant rebalanced portfolio in hindsight (BCRP) on five out of the six real-world financial datasets. Notably, ASCM achieves a 67-fold improvement over BCRP in cumulative wealth on the TSE dataset.