Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection

By Philippe Naveau, Raphaël Huser, P. Ribéreau, Alexis Hannart. 2016

189 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/932849ca48131ea0daede7570155372a4c1fcd0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Global and Planetary Change

Citations in the corpus

Stochastic generation of multi-site daily precipitation focusing on extreme events (2018, methodology): “Since the tail of the distribution on precipitation amounts at each station will dictate the generation of the most extreme precipitation events, important features of GWEX are as follows: – the application of a heavy-tailed distribution to precipitation amounts at each station (Naveau et al., 2016)”
Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures (2016, result): “…of extremes (see, e.g., Thibaud et al., 2013; Huser and Davison, 2014; Wadsworth and Tawn, 2014), and results from Thibaud and Opitz (2015) and Huser et al. (2016) suggest it provides a reasonable compromise between bias and variance compared to alternative approaches to ﬁt threshold…”
Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate (2018, background): “Methods in the third category are based on mixtures of a GPD for the tail and another distribution for the “bulk” joined at the threshold [e.g., MacDonald et al. (2011), Wadsworth and Tawn (2012), Naveau et al. (2016)].”
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018, background): “In future work, it would be interesting to replace the generalized Pareto distribution by more ﬂexible sub-asymptotic tail models (see, e.g., Papastathopoulos and Tawn, 2013; Naveau et al. , 2016), which could potentially be applied at much lower thresholds.”
Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount (2019, unknown): “In hydrology, there is a long history of using the so-called Probability Weighted Moments (PWM) to infer the parameters of a GPD (see, e.g., Hosking and Wallis, 1987; Ribereau et al. , 2011; Naveau et al. , 2016; Carreau et al. , 2017).”