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pro vyhledávání: '"Einmahl, John H. J."'
Autor:
Berthet, Philippe, Einmahl, John H. J.
Given $n$ independent random vectors with common density $f$ on $\mathbb{R}^d$, we study the weak convergence of three empirical-measure based estimators of the convex $\lambda$-level set $L_\lambda$ of $f$, namely the excess mass set, the minimum vo
Externí odkaz:
http://arxiv.org/abs/2006.02229
The statistical theory of extremes is extended to observations that are non-stationary and not independent. The non-stationarity over time and space is controlled via the scedasis (tail scale) in the marginal distributions. Spatial dependence stems f
Externí odkaz:
http://arxiv.org/abs/2003.04265
Autor:
Einmahl, John H. J., Segers, Johan
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail copula and the stable tail dependence function are equivalent ways to capture the dependence in the upper tail. The empirical versions of these function
Externí odkaz:
http://arxiv.org/abs/2001.11408
Akademický článek
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Autor:
Ahmed, Hanan, Einmahl, John H. J.
Heavy tailed phenomena are naturally analyzed by extreme value statistics. A crucial step in such an analysis is the estimation of the extreme value index, which describes the tail heaviness of the underlying probability distribution. We consider the
Externí odkaz:
http://arxiv.org/abs/1807.06470
Consider a random sample from a continuous multivariate distribution function $F$ with copula $C$. In order to test the null hypothesis that $C$ belongs to a certain parametric family, we construct an empirical process on the unit hypercube that conv
Externí odkaz:
http://arxiv.org/abs/1710.11504
Publikováno v:
Natural Hazards (2018)
The area-characteristic, maximum possible earthquake magnitude $T_M$ is required by the earthquake engineering community, disaster management agencies and the insurance industry. The Gutenberg-Richter law predicts that earthquake magnitudes $M$ follo
Externí odkaz:
http://arxiv.org/abs/1709.07662
Likelihood-based procedures are a common way to estimate tail dependence parameters. They are not applicable, however, in non-differentiable models such as those arising from recent max-linear structural equation models. Moreover, they can be hard to
Externí odkaz:
http://arxiv.org/abs/1601.04826
Publikováno v:
Annals of Statistics 2015, Vol. 43, No. 6, 2738-2765
Statistical depth measures the centrality of a point with respect to a given distribution or data cloud. It provides a natural center-outward ordering of multivariate data points and yields a systematic nonparametric multivariate analysis scheme. In
Externí odkaz:
http://arxiv.org/abs/1510.08694
Publikováno v:
Annals of Statistics 2015, Vol. 43, No. 2, 878-902
Let $(X_1,Y_1),\ldots,(X_n,Y_n)$ be an i.i.d. sample from a bivariate distribution function that lies in the max-domain of attraction of an extreme value distribution. The asymptotic joint distribution of the standardized component-wise maxima $\bigv
Externí odkaz:
http://arxiv.org/abs/1504.00465