Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Richards, Jordan"'
In econometrics, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficiency drops when it undergoes extreme events. Many models for multivaria
Externí odkaz:
http://arxiv.org/abs/2408.06661
The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to modelling the extremes of multivariate data. These shapes, termed limi
Externí odkaz:
http://arxiv.org/abs/2406.19936
Autor:
Richards, Jordan, Huser, Raphaël
Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management in statistical finance, econometrics, and actuarial sciences. Interest
Externí odkaz:
http://arxiv.org/abs/2404.09154
Autor:
Richards, Jordan, Alotaibi, Noura, Cisneros, Daniela, Gong, Yan, Guerrero, Matheus B., Redondo, Paolo, Shao, Xuanjie
Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We pre
Externí odkaz:
http://arxiv.org/abs/2311.11054
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators
Externí odkaz:
http://arxiv.org/abs/2310.02600
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is
Externí odkaz:
http://arxiv.org/abs/2308.14547
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neu
Externí odkaz:
http://arxiv.org/abs/2306.15642
Extreme wildfires are a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely
Externí odkaz:
http://arxiv.org/abs/2212.01796
Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Max-stable processes are common choices for modeling spatially-indexed block maxima, where an assumption of stationarity is usual to make inference feasible
Externí odkaz:
http://arxiv.org/abs/2210.05792
Autor:
Richards, Jordan, Huser, Raphaël
Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describ
Externí odkaz:
http://arxiv.org/abs/2208.07581