Rank-based estimation under asymptotic dependence and independence, with applications to spatial extremes
DOI10.1214/20-AOS2046zbMath1486.62142arXiv2008.03349OpenAlexW3213752499MaRDI QIDQ2054519
Sebastian Engelke, Michaël Lalancette, Stanislav Volgushev
Publication date: 3 December 2021
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2008.03349
multivariate extremesasymptotic independencespatial processM-estimationinverted max-stable distribution
Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to environmental and related topics (62P12) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)
Cites Work
- Unnamed Item
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- Extremes on river networks
- When uniform weak convergence fails: empirical processes for dependence functions and residuals via epi- and hypographs
- Dependence structure of risk factors and diversification effects
- An M-estimator for tail dependence in arbitrary dimensions
- Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribu\-tion
- Parametric tail copula estimation and model testing
- Bootstrap approximation of tail dependence function
- A method of moments estimator of tail dependence
- Stationary max-stable fields associated to negative definite functions
- Best attainable rates of convergence for estimators of the stable tail dependence function
- Models for stationary max-stable random fields
- Hidden regular variation, second order regular variation and asymptotic independence
- Bayesian inference for multivariate extreme value distributions
- Bivariate tail estimation: dependence in asymptotic independence
- Approximation of intermediate quantile processes
- Maxima of normal random vectors: Between independence and complete dependence
- On convergence toward an extreme value distribution in \(C[0,1\)]
- Estimation of the coefficient of tail dependence in bivariate extremes
- On second order conditions in the multivariate block maxima and peak over threshold method
- Extremal dependence of random scale constructions
- Multiplier bootstrap of tail copulas with applications
- Limit laws for random vectors with an extreme component
- Dependence modelling for spatial extremes
- A New Class of Models for Bivariate Joint Tails
- Bivariate extreme value theory: Models and estimation
- Statistics for near independence in multivariate extreme values
- Extreme values of independent stochastic processes
- Non-Parametric Bayesian Inference on Bivariate Extremes
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- Graphical Models for Extremes
- Modeling Spatial Processes with Unknown Extremal Dependence Class
- Likelihood-Based Inference for Max-Stable Processes
- Geostatistics of extremes
- Modelling Across Extremal Dependence Classes
- Estimation of Hüsler–Reiss Distributions and Brown–Resnick Processes
- A sum characterization of hidden regular variation with likelihood inference via expectation-maximization
- Convex Analysis
- An M-Estimator of Spatial Tail Dependence
- Dependence measures for extreme value analyses
- Statistical modeling of spatial extremes
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