没有合适的资源?快使用搜索试试~ 我知道了~
Precise large deviations for sums of two-dimensional random vect...
0 下载量 4 浏览量
2019-12-28
23:54:21
上传
评论
收藏 447KB PDF 举报
温馨提示
二维重尾相依风险模型的精细大偏差,田海兰,沈新美,令${ec{X}_{k}, k geq 1}$为一列独立同分布二维非负随机向量, 边际分布为F1, F2, 均服从$ERV$族, 联合分布为F1,2, 有限均值向量 $ec{mu}=epec{X}_{1}$
资源推荐
资源详情
资源评论
˖ڍመڙጲ
http://www.paper.edu.cn
二维重尾相依风险模型的精细大偏差
田海兰 ,沈新美
大连理工大学 数学科学学院,大连 116024
摘要:令{
~
X
k
, k ≥ 1}为一列独立同分布二维非负随机向量, 边际分布为F
1
, F
2
, 均服从ERV 族,
联合分布为F
1,2
, 有限均值向量~µ = E
~
X
1
. 本文允许
~
X
1
的分量相依. 在一些温和的条件下, 本文
讨论了部分和
~
S
n
=
P
n
k=1
~
X
k
和随机和
~
S
N(t)
=
P
N(t)
k=1
~
X
k
的精细大偏差, 其中N(t)是一个
与{
~
X
k
, k ≥ 1}相互独立的计数过程
关键词:精细大偏差, ERV族,连接函数,随机和
中图分类号: 60G50; 62P05; 60F10.
Precise large deviations for sums of
two-dimensional random vectors with dependent
components with extended regularly varying tails
TIAN Hai-Lan , SHEN Xin-Mei
School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024
Abstract: Let {
~
X
k
, k ≥ 1} be a sequence of independent identically distributed non-negative
random vectors with common marginal distributions F
1
, F
2
having extended regularly varying
tails, joint distribution function F
1,2
and finite mean ~µ = E
~
X
1
. The two components of
~
X
1
are allowed to be dependent. Under some mild assumptions, precise large deviations for both
the partial sums
~
S
n
=
P
n
k=1
~
X
k
and the random sums
~
S
N(t)
=
P
N(t)
k=1
~
X
k
are investigated,
where N(t) is a counting process independent of the sequence {
~
X
k
, k ≥ 1}.
Key words: precise large deviations, extended regular variation distributions, copula
functions, random sums.
0 Introduction
Large deviation probabilities occur in a natural way in many applied areas, for instance,
insurance and finance. Up to now, many researchers have made great efforts of the precise
基金项目: Fund for the Doctor Program of Higher Education(No. 20100041120038)
作者简介: TIAN Hai-Lan(1986-),female,postgraduate,major research direction:Heavy tailed distributions
in the presence of dependence. Correspondence author:SHEN Xin-Mei(1981-),female,lecturer,major research
direction:Risk theory.
- 1 -
˖ڍመڙጲ
http://www.paper.edu.cn
large deviations for the loss process of a classical insurance risk model and have obtained a
lot of inspiring results. Mainstream research on precise large deviation probabilities has been
concentrated on the study of the asymptotic
P
N(t)
X
k=1
X
k
− λ(t)µ > x
∼ λ(t)
¯
F (x), (1)
which holds uniformly for some x-region, where {X
k
, k ≥ 1} is a sequence of independent
and identically distributed nonnegative random variables with common distribution function
F (x) = P(X ≤ x),
¯
F (x) = P(X
1
> x), µ = EX
1
, and N(t) is a nonnegative integer valued
counting process independent of {X
k
, k ≥ 1}, λ(t) = EN (t). Here and henceforth, all limit
relationships, unless otherwise stated, are as n → ∞ or as t → ∞, and the uniformity in (1) is
understood in the following sense:
lim
t→∞
sup
x≥γλ(t)
P(S
N(t)
− λ(t)µ > x)
λ(t)
¯
F (x)
− 1
= 0,
where γ is an arbitrary constant. For the classical results, we refer the reader to Cline and
Hsing (1991), Cline and Samorodnitsky (1994), Kl¨uppelberg and Mikosch (1997) and Ng et
al. (2004).
In risk theory, heavy-tailed distributions (no finite exponential moments) are often used
to model large claim amounts. They play a key role in some fields such as insurance, financial
mathematics, and queueing theory. A distribution function F is said to belong to the extended
regular variation class, if for some 0 < α ≤ β < ∞,
v
−β
≤ lim inf
x→∞
¯
F (vx)
¯
F (x)
≤ lim sup
x→∞
¯
F (vx)
¯
F (x)
≤ v
−α
, for all v ≥ 1, (2)
we denote F ∈ ERV (−α, −β). If α = β , we denote F ∈ R
−α
(regular variation class). In
addition to the above classes, there are some other important classes, such as the consistent
variation class C, the dominant variation class D, and the subexponential class S. These classes
satisfy the following relationships:
R
−α
⊂ ERV (−α, −β) ⊂ C ⊂ D ∩ S.
Moreover, if F ∈ ERV (−α, −β), then from Proposition 2.2.1 of Bingham et al. (1987), we
know that for any ρ > β, there are positive constants x
0
and B such that
¯
F (y)
¯
F (x)
≤ B
x
y
ρ
, (3)
for all x ≥ y ≥ x
0
. For more details about the classes of heavy-tailed distributions and
applications, we refer the reader to Bingham et al. (1987), and Embrechts et al. (1997).
In recent years, the large deviations problem of multi-dimensional risk models has been
capturing the eyes of some researchers, see, for example, Wang and Wang (2007). In real
- 2 -
剩余12页未读,继续阅读
资源评论
weixin_38640117
- 粉丝: 1
- 资源: 926
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功