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Moments-type Estimation of Bivariate Extreme Value Copulas Based...
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基于连接函数的二元极值Copula的矩估计,黄超,林金官,本文研究了二元极值Copula的矩估计问题。通常极值Copula可以由一个Pickands相依函数表示,因此本文基于连接函数针对Pickands相依函数提出一�
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˖ڍመڙጲ
http://www.paper.edu.cn
基于连接函数的二元极值 Copula 的矩估计
黄超 ,林金官
东南大学数学系,南京 210096
摘要:本文研究了二元极值 Copula 的矩估计问题。通常极值 Copula 可以由一个 Pickands 相
依函数表示,因此本文基于连接函数针对 Pickands 相依函数提出一种二元极值 Copula 的矩
估计方法,该方法可以看作是现有非参数估计方法的一个推广。文章通过数值模拟给出了该方
法的有限样本性质。同时,本文还应用了参数方法和矩估计方法分析了两个不同位置的最大周
末车速数据。
关键词:二元极值 copula; Pickands 相依函数; 连接函数; 普通最小二乘法.
中图分类号: O212.1
Moments-type Estimation of Bivariate Extreme
Value Copulas Based on Link Functions
HUANG Chao , LIN Jin-guan
Department of Mathematics, Southeast University, Nanjing 210096
Abstract: This paper considers the moments-type estimation of bivariate extreme value
copulas. Usually the inference on an extreme value copula proceeds via its Pickands
dependence function. In this paper a new estimation method for the Pickands dependence
function is proposed based on link functions, which can be treated as an extension of the
existing nonparametric estimates. The finite sample performance is investigated by simulation
studies, and the maximum weekend car speed data registered at two given locations is
analyzed via parametric method and our proposed method.
Key words: Bivariate extreme value copula; Pickands dependence function; Link function;
Ordinary least squares.
基金项目: The research is supported by The Research Fund for the Doctoral Program of Higher Education of China
(20120092110021),National Natural Science Foundation of China (11171065),National Natural Science Foundation of China
(11201229),Natural Science Foundation of Jiangsu Province (BK2011058),Youth Foundation for Humanities and Social
Sciences Project from Ministry of Education of China (11YJC790311),and the Fundamental Research Funds for the Central
Universities.
作者简介: Huang Chao(1985-),male,PhD student,major research direction:statistics of extreme value. E-mail:
huangchao.seu@hotmail.com. Correspondence author:Lin Jin-guan (1964-),male,professor,major research direction:
longitudinal data analysis, Copula. E-mail: jglin@seu.edu.cn.
- 1 -
˖ڍመڙጲ
http://www.paper.edu.cn
0 Introduction
Copulas are functions that join multivariate distributions to their marginal distribution
function, and every continuous multivariate distribution can be expressed in terms of its margins
and a unique copula [1,2]. For the bivariate extreme value case, it is concerned with distributions
related to the componentwise maximums of a given i.i.d. random sample and several approaches
have been developed to model the bivariate extreme data [3–5]. Therefore, the bivariate extreme
value distribution is characterized by the univariate extreme-value distributions, which are their
marginal distributions, and a bivariate extreme-value copula. The bivariate extreme-value
copula is the limits of copulas of componentwise maxima in independent random samples, and
it usually provides appropriate models for the dependence structure between rare events.
Since an extreme value copula can be characterized by its Pickands dependence function
or tail dependence function, usually the inference on an extreme value copula proceeds via its
Pickands dependence function. There are two main families of nonparametric estimators: the
Pickands type estimator [3] and the Cap
�
era
�
a-Foug
�
eres-Genest (CFG) type estimator [6]. Many
existing paper present various nonparametric improvement versions for the two main families
of estimators [7, 8].
The motivation of our studies is to propose a new moment-type estimator for the bivari-
ate extreme value copula when the margins are known. In this paper this new estimator is
proposed based on the moment of random variables constructed by link functions. It includes
the Pickands type and the CFG type estimators as special cases by choosing the link func-
tions properly. The ordinary least squares technique [9] and projection methodology [10] are
adopted to improve the proposed estimator. The weak convergence of the proposed estimator
is demonstrated and the finite sample performance is investigated by simulation studies. It
will be found that the proposed estimator not only has a sound asymptotic behavior but also
performs better than the Pickands and CFG type ones for both modest and small sample sizes
when the link function is properly chosen.
The format of the paper is as follows. Section 1 introduces the bivariate extreme value
copula and the Pickands dependence function. The new nonparametric estimator based on the
link function is proposed, which is named L-F estimator later. The weak convergence of the
proposed estimator is demonstrated in Section 2. In Section 3 some simulations are performed
to show the finite sample performance of the new estimator, and the comparisons between
the new one and the previous existing ones are implemented via Monte Carlo simulations. At
last, the maximum weekend car speed data registered at two given locations is analyzed via
parametric and nonparametric methods in Section 4.
- 2 -
˖ڍመڙጲ
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1 Moment-type estimation of bivariate extreme value
copulas
1.1 Extreme value copula and Pickands dependence function
Consider a sequence of i.i.d. random vectors X
i
= (X
i1
, X
i2
), i = 1, ··· , n from d-variate
extreme value distribution function F with margins F
1
, F
2
and the bivariate extreme value
copula C
F
. It is well known that [11], the margins belong to the generalized extreme value
(GEV) distribution family for sample maxima:
F
i
(x, θ
i
) = exp
−
1 + ξ
i
x − µ
i
σ
i
−1/ξ
i
, 1 + ξ
i
x − µ
i
σ
i
> 0,, i ∈ { 1, 2}, (1)
where θ
i
= (ξ
i
, σ
i
, µ
i
). According to [1], the bivariate extreme value distribution function F
can be written as
F (x) = C
F
(F
1
(x
1
), F
2
(x
2
)), x ∈ R
2
. (2)
The bivariate copula C
F
is an extreme value copula if and only if there exists a finite Borel
measure H on the unit simplex ∆ =
(w
1
, w
2
) ∈ [0, ∞)
2
:
j
w
j
= 1
, called spectral measure,
such that
C
F
(u
1
, u
2
) = exp (−l(−log u
1
, −log u
2
)) , (u
1
, u
2
) ∈ (0, 1]
2
, (3)
where l : [0, ∞)
2
→ [0, ∞) is called the tail dependence function, given by
l(z
1
, z
2
) =
∆
2
j=1
(w
j
z
j
)dH(w
1
, w
2
), (z
1
, z
2
) ∈ [0, ∞)
2
. (4)
The spectral measure H is arbitrary except for the two moment constraints
∆
w
j
dH(w
1
, w
2
) = 1, j ∈ {1, 2}. (5)
The tail dependence function l in (4) is convex, and satisfies l(cx
1
, cx
2
) = cl(x
1
, x
2
) for c > 0,
max(x
1
, x
2
) ≤ l(x
1
, x
2
) ≤ x
1
+ x
2
for all (x
1
, x
2
) ∈ [0, ∞)
2
.
The Pickands dependence function A : ∆ → [1/2, 1], is simply the restriction of l to the
unit simplex:
A(w
1
, w
2
) =
l(z
1
, z
2
)
(z
1
+ z
2
)
, where w
j
=
z
j
z
1
+ z
2
, (6)
for (z
1
, z
2
) ∈ [0, ∞)
2
\{0}. Obviously, according to the characters of tail dependence function
l, the function A is convex as well and satisfies max(w
1
, w
2
) ≤ A(w
1
, w
2
) ≤ 1. Then based on
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