Results 21 to 30 of about 180 (55)
Multiplier bootstrap of tail copulas - with applications [PDF]
, 2011 In the problem of estimating the lower and upper tail copula we propose two bootstrap procedures for approximating the distribution of the corresponding empirical tail copulas.
Bücher, Axel, Dette, Holger
core
Dynamic Bivariate Normal Copula
, 2015 Normal copula with a correlation coefficient between $-1$ and $1$ is tail independent and so it severely underestimates extreme probabilities. By letting the correlation coefficient in a normal copula depend on the sample size, H\"usler and Reiss (1989 ...
Liao, Xin +3 more
core +1 more source
Rank-based inference for bivariate extreme-value copulas
, 2008 Consider a continuous random pair $(X,Y)$ whose dependence is characterized by an extreme-value copula with Pickands dependence function $A$. When the marginal distributions of $X$ and $Y$ are known, several consistent estimators of $A$ are available ...
Genest, Christian, Segers, Johan
core +1 more source
Estimating Extreme Bivariate Quantile Regions [PDF]
AMS 2000 subject classifications. Primary 62G32, 62G05; secondary 60G70, 60F05.
Einmahl, J.H.J. +2 more
core +1 more source
Understanding heavy tails in a bounded world or, is a truncated heavy tail heavy or not? [PDF]
, 2009 We address the important question of the extent to which random variables and vectors with truncated power tails retain the characteristic features of random variables and vectors with power tails. We define two truncation regimes, soft truncation regime
Chakrabarty, Arijit +1 more
core +1 more source
Extreme quantile estimation for partial functional linear regression models with heavy-tailed distributions. [PDF]
Can J Stat, 2022 Zhu H, Li Y, Liu B, Yao W, Zhang R.
europepmc +1 more source
Multivariate Tail Coefficients: Properties and Estimation. [PDF]
Entropy (Basel), 2020 Gijbels I, Kika V, Omelka M.
europepmc +1 more source
Characterizations in a random record model with a non-identically distributed initial record [PDF]
We consider a sequence of random length M of independent absolutely continuous observations Xi, 1 = i = M, where M is geometric, X1 has cdf G, and Xi, i = 2, have cdf F. Let N be the number of upper records and Rn, n = 1, be the nth record value. We show
Gadi Barlevy, H. N. Nagaraja
core
Weighted Approximations of Tail Copula Processes with Application to Testing the Multivariate Extreme Value Condition [PDF]
Consider n i.i.d. random vectors on R2, with unknown, common distribution function F.Under a sharpening of the extreme value condition on F, we derive a weighted approximation of the corresponding tail copula process.Then we construct a test to check ...
Einmahl, J.H.J., Haan, L.F.M. de, Li, D.
core +1 more source