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作者：Einmahl, John H. J.; Kiriliouk, Anna; Krajina, Andrea; Segers, Johan
作者单位：Tilburg University; Universite Catholique Louvain; University of Gottingen
摘要：Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular m...
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作者：Einmahl, John H. J.; de Haan, Laurens; Zhou, Chen
作者单位：Tilburg University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Universidade de Lisboa; European Central Bank; De Nederlandsche Bank NV
摘要：We extend classical extreme value theory to non-identically distributed observations. When the tails of the distribution are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated non-parametrically along with the (common) extreme value index. For a positive extreme value index, joint asymptotic normality of both estimators is shown; they are asymptotically independent. We also establish asymptotic normality of a forecasted high...
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作者：Yang, Fan; Small, Dylan S.
作者单位：University of Chicago; University of Pennsylvania
摘要：Many clinical studies on non-mortality outcomes such as quality of life suffer from the problem that the non-mortality outcome can be censored by death, i.e. the non-mortality outcome cannot be measured if the subject dies before the time of measurement. To address the problem that this censoring by death is informative, it is of interest to consider the average effect of the treatment on the non-mortality outcome among subjects whose measurement would not be censored under either treatment or...
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作者：Rajaratnam, Bala; Roberts, Steven; Sparks, Doug; Dalal, Onkar
作者单位：Stanford University; Australian National University
摘要：The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, statistical properties of high dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, co-ordinatewise methods, which are the most common means of computing the lasso solution, naturally work well in the presence of low-t...
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作者：Genton, Marc G.; Hall, Peter
作者单位：King Abdullah University of Science & Technology; University of Melbourne; University of California System; University of California Davis
摘要：We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by 'tilting', or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a giv...
