HILL B M. A Simple General Approach to Inference about the Tail of a Distribution [J]. The Annals of Statistics, 1975, 3(5): 1163-1174.
DE HAAN L, FERREIRA A. Extreme Value Theory [M]. New York: Springer, 2006.
RESNICK S I. Heavy-Tail Phenomena[M]. New York: Springer, 2007: 39-69.
CAEIRO F, GOMES M I. A Class of Asymptotically Unbiased Semi-Parametric Estimators of the Tail Index [J]. Test, 2002, 11(2): 345-364. doi: 10.1007/BF02595711
GOMES M I, MARTINS M J, NEVES M. Alternatives to a Semi-parametric Estimator of Parameters of Rare Events-the Jackknife Methodology[J]. Extremes, 2000, 3(3): 207-229. doi: 10.1023/A:1011470010228
KINSVATER P, FRIED R, LILIENTHAL J. Regional Extreme Value Index Estimation and a Test of Tail Homogeneity [J]. Environmetrics, 2016, 27(2): 103-115. doi: 10.1002/env.2376
STUPFLER G. On a Relationship Between Randomly and Non-Randomly Thresholded Empirical Average Excesses for Heavy Tails [J]. Extremes, 2019, 22(4): 749-769. doi: 10.1007/s10687-019-00351-5
胡爽, 彭作祥. 基于分块思想的Pickands型估计量[J]. 西南大学学报(自然科学版), 2019, 41(5): 53-58.
AHMED H, EINMAHL J H J. Improved Estimation of the Extreme Value Index Using Related Variables [J]. Extremes, 2019, 22(4): 553-569. doi: 10.1007/s10687-019-00358-y
PICKANDS J. Statistical Inference Using Extreme Order Statistics [J]. The Annals of Statistics, 1975, 3(1): 119-131.
DEKKERS A L M, EINMAHL J H J, DE HAAN L. A Moment Estimator for the Index of an Extreme-Value Distribution [J]. The Annals of Statistics, 1989, 17(4): 1833-1855.
SCHMIDT R, STADTMVLLER U. Non-Parametric Estimation of Tail Dependence [J]. Scandinavian Journal of Statistics, 2006, 33(2): 307-335. doi: 10.1111/j.1467-9469.2005.00483.x
DREES H, HUANG X. Best Attainable Rates of Convergence for Estimators of the Stable Tail Dependence Function [J]. Journal of Multivariate Analysis, 1998, 64(1): 25-46. doi: 10.1006/jmva.1997.1708
HOGA Y. Detecting Tail Risk Differences in Multivariate Time Series [J]. Journal of Time Series Analysis, 2018, 39(5): 665-689. doi: 10.1111/jtsa.12292
BILLINGSLEY P. Convergence of Probability Measures [M]. Hoboken: John Wiley & Sons, 1999.