On the estimation of the second order parameter in extreme-value...
Transcript of On the estimation of the second order parameter in extreme-value...
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On the estimation of the second order parameter inextreme-value theory
By
El Hadji DEME
Joint work
Laurent GARDES & Stephane GIRARD
Strasbourg March 6th 2012
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
1 Extreme-Value Theory
2 New family of estimators for the second order parameter
3 Asymptotic properties
4 Link with existing estimators
5 Numerical results
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Main results on extreme value theory
Let X1, . . . ,Xn be a sequence of independent copies ofa real random variable (r.v.) X with cumulativedistribution function F. The order statisticsassociated to this sample are denoted by :X1,n ≤ · · · ≤ Xn,n.
Fisher-Tippett-Gnedenko theorem
Under some conditions of regularity on F, thereexists a real parameter γ and two sequences(an)n≥1 > 0 and (bn)n≥1 ∈ R such that for all x ∈ R,
limn→∞
P(a−1n (Xn,n − bn) ≤ x
)= EVγ(x),
with EVγ(x) =
(exp
“−(1 + γx)
−1/γ+
”if γ 6= 0,
exp`−e−x
´if γ = 0,
where y+ = max(y , 0).3 / 38
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Definition :
� The parameter γ is the tail index, the primary parameter of
extreme events.
� EVγ is called the extreme value distribution and F is then said
to belong to the domain of attraction of EVγ (F ∈ DA(EVγ)).
Heavy-tailed models
� In statistics of Extremes, a model F is said to beheavy-tailed whenever, for some γ > 0, its survivalfunction is of the forme :
1− F (x) = x−1/γ`F (x)⇔ U(x) = xγ`U(x)
where U(x) = inf{y : F (y) ≥ 1− 1/x} is quantilefunction and `•(·) is a slowly varying function i.e.`•(λx)/`•(x)→ 1 as x →∞ for all λ ≥ 1.The present model is now often restated as the asumption of F isregulary varying at infinity with index −1/γ (denoted1− F (x) ∈ RV−1/γ).
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Exemples of heavy-tailed models
� Strict Pareto distribution
1− F (x) = x−α, x > 1; α > 0.
is heavy-tailed with γ = 1/α and `F (x) = 1.
� F (m, n) distribution
f (x) =Γ`
m+n2
´Γ`
m2
´Γ`
n2
´ “m
n
”m/2
xm/2−1“
1 +m
nx”−(m+n)/2
x > 0; m, n > 0
is heavy-tailed with γ = 2/n and
`F (x) =Γ`
m+n2
´Γ`
m2
´Γ`
n2
´ “m
n
”m/2
xm/2−1
„m
n+
1
x
«−(m+n)/2
(1 + o(1))
for x →∞.
� Others : |t|, log-gamma, inverse gamma, Frechet, Burr,...
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Inference statistic of γ for heavy-tailed model
In Statistics of extremes, inference is often based
�Wi,k = (log Xn−i+1,n − log Xn−k,n) the log-excesses
�Zi,k = i (log Xn−i+1,n − log Xn−i,n) the rescaled log-spacings
Exemples of γ estimators
Hn,k =1
k
kXi=1
Wi,k =1
k
kXi=1
Zi,k , Hill Estimator, Hill (1975)
γn =M(1)n,k + 1− 1
2
0B@1−
“M(1)
n,k
”2
M(2)n,k
1CA−1
Moment Estmator,
Deker et al (1989) with M(α)n,k =
1
k
kXi=1
W αi,k
Others Estimators of γ, received a lot of attention Smith (1987), Csorgoet al. (1985), Schultze and Steinebach (1996), Kratz and Resnick (1996),
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Asymptotic behavior
� Since F ∈ RV−1/γ , then if k →∞ and k/n→ 0 as n→∞,
then Hn,kP−→ γ, γn
P−→ γ and M(α)n,k /µ
(1)α
P−→ γα with µ(1)α = Γ(α + 1)
� Asymptotic normality ?
The asymptotic distribution of estimators of γ is
obtained under a second order condition.
Second Order Condition (S.O.C)
There exist a function A(x)→ 0 and a second orderparameter ρ ≤ 0 such that, for every x > 0,
limt→∞
log U(tx)− log U(t)− γ log x
A(t)=
xρ − 1
ρ.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Second Order Condition
Remarks
Under the regularly variation condition
logU(tx)
U(t)→ γ log x for, t →∞
So the (S.O.C) specifies the rate of this convergence.
|A| is regularly varying with index ρ.
Exemple : Hall class of Heavy-tailed models
� Hall class (Hall and Welsh, (1985))
U(x) = Cxγ(1 + Dxρ + o(xρ)), (x →∞)
with C > 0, satisfies the second order condition with
A(x) = ρDxρ.
Exemples : Frechet, Burr, Generalized Pareto (GP),|t|,...8 / 38
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Asymptotic representation of γ’s estimators
Hill’s estmator
Hn,kD= γ +
γ√kNk +
A(n/k)
1− ρ (1 + oP(1))
Moment’s estimator
M(α)n,k
D= γαµ(1)
α +γασ
(1)α√k
P(α)k + αγα−1µ(2)
α A(n/k)(1 + oP(1))
where Nk and P(α)k are asymptotically standard
normal random variables,
µ(2)α =
α
ρ
1− (1− ρ)α
(1− ρ)αand σ(1)
α =p
Γ(2α + 1)− Γ2(α + 1).
The Hill’s estimator Hn,k correspond toM(1)n,k
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Asymptotic normality
if k, n→∞ with k/n→ 0 and√
kA(n/k)→ λ ∈ R, then
√k(Hn,k − γ)
D−→ N„
λ
1− ρ , γ2
«and √
k(M(α)n,k − γ
αµ(1)α )
D−→ N„λαγα−1µ(2)
α ,“γασ(1)
α
”2«
Comment
� ρ controls the bias of the estimators of γ
� How to estimate the second order tail parameter ρ ?
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
New family of estimators for the second order parameter
The model
Tn = Tn(X1, ...,Xn) : a random vector in Rd drawn fromthe sample X1, . . . ,Xn.The statistics can always be expanded as :
ω−1n (Tn − χnI)
P−→ f (ρ)
whereI = t(1, . . . , 1) ∈ Rd ,
χn and ωn : random variables,
ξn ∈ Rd : a random vector,
f : R− → Rd : a function continuously differentiablein a neighborhood of ρ (independent of γ).
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
General approach
Notations
ψ : Rd → R such that
� Invariance properties (Inv-prop)
ψ(x + λI) = ψ(x) for all x ∈ Rd and λ ∈ R,
ψ(λx) = ψ(x) for all λ ∈ R \ {0},
� Regularity properties (Reg-prop) ψ is continuously
differentiable in a neighborhood of f (ρ),
ϕ := ψ ◦ f is continuous in a neighborhood of ρ
� Bijection property (Bij-prop)
there exist J0 ⊆ R− and an open interval J ⊂ R such that ϕ isa bijection from J0 to J.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
The estimator
Clearly
by the invariance and the regularity properties
� ψ(ω−1n (Tn − χnI)) = ψ(Tn)
P−→ ψ(f (ρ)).
� Zn = ψ(Tn) ≈ ϕ(ρ).
Under the bijection property, our family ofestimators of the second order parameter is thusdefined by :
ρn = ϕ−1(Zn)1l{Zn ∈ J}.
1lA is the indicator function of the set A.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Asymptotic properties
Theorems
Suppose that Inv-prop, Reg-prop and Bij-prop holdthen
ρnP−→ ρ as n→∞
if there exist a sequence vn →∞, a function,m : R− → Rd and a d × d matrix Σ such that
vn(ω−1n (Tn − χnI)− f (ρ))
D−→ Nd(m(ρ),Σ)
then
vn(ρn − ρ)D−→ N
mψ (ρ)
ϕ′(ρ),σ2ψ(ρ)
(ϕ′(ρ))2
!with,
� ϕ′(ρ) = t f ′(ρ)∇ψ(f (ρ)),
�mψ(ρ) = tm(ρ)∇ψ(f (ρ)),
� σ2ψ(ρ) = t∇ψ(f (ρ)) Σ ∇ψ(f (ρ)).
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
1. Estimators based on rescaled log-spacings : log Xn−j+1 − log Xn−j
� Kernel estimators
Rk(τ) =1
k
kXj=1
Hτ
„j
k + 1
«j log
Xn−j+1,n
Xn−j,n=
1
k
kXj=1
Hτ
„j
k + 1
«Zi,k ,
Hτ is a kernel function such thatZ 1
0
Hτ (u)du = 1.
This statistic is used for instance by Beirlant etal., (Extremes, 1999) to estimate the extreme valueindex γ and by Goegebeur et al. (JSPI, 2010) toestimate the second order parameter ρ.
They proved asymptotic normality of theseestimators under a technical condition on thekernel, denoted by (C1) hereafter and under.For the asymptotic normality of ρ estimators theyuse a third order condition.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Third Order Condition
Third Order Condition (T.O.C)
There exist functions A(x)→ 0 and B(x)→ 0, a secondorder parameter ρ ≤ 0 and a third order parameterβ ≤ 0 such that, for every λ > 0,
limt→∞
log U(tx)−log U(t)−γ log xA(t)
− xρ−1ρ
B(t)=
1
β
„xρ+β − 1
ρ+ β− xρ − 1
ρ
«,
where the functions |A| and |B| are regularly varyingwith index ρ and β respectively.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Links with existing estimators
Link with our framework
Suppose the third order condition and (C1) hold. Ifthe sequence k satisfies
k →∞, n/k →∞, k1/2A(n/k)→∞,
k1/2A2(n/k)→ λA and k1/2A(n/k)B(n/k)→ λB ,
then the random vector
T (R)n :=
“(Rk(τi )/γ)θi , i = 1, . . . , d
”,
satisfies the model i.e. ω−1n (T
(R)n − χnI)
P−→ f (R)(ρ) withχn = 1,
ωn = A(n/k)/γ(1 + oP (1)),
f (R)(ρ) =
„θi
Z 1
0
Hτi (u)u−ρdu, i = 1, . . . , d
«
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
Link with our framework
d = 8, ψδ : D 7→ R \ {0}
ψδ(x1, . . . , x8) = eψδ(x1 − x2, x3 − x4, x5 − x6, x7 − x8), where δ ≥ 0
D = {(x1, . . . , x8) ∈ R8; x1 6= x2, x3 6= x4, and (x5 − x6)(x7 − x8) > 0}.
eψδ : R4 7→ R is given by : eψδ(y1, . . . , y4) =y1
y2
„y4
y3
«δ.
Hτi , i = 1, ..., 8, the statistic T(R)n depends on 16
parameters {(θi , τi ) ∈ (0,∞)2, i = 1, . . . , 8}.
Let θ = (θ1, . . . , θ4) ∈ (0,∞)4 with θ3 6= θ4 and consider
� {θi = θdi/2e, i = 1, . . . , 8} with δ = (θ1 − θ2)/(θ3 − θ4) and
dxe = inf{n ∈ N|x ≤ n}.�τ1 < τ2 ≤ τ3 < τ4, τ5 < τ6 ≤ τ7 < τ8
T(R)n involves 12 free parameters. Z
(R)n = ψδ(T
(R)n ) and
ϕ(R)δ = ψδ ◦ f (R).
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
Link with our framework
Z(R)n does not depend on the unknown parameter.
We can thus define the following family of estimators :
ρ(R)n = ϕ−1
δ 1l{Z (R)n ∈ JR}.
Let denote m(R)A =
“m
(R,i)A , i = 1, ..., 4
”, m
(R)B =
“m
(R,i)B , i = 1, ..., 4
”and v (R) =
“v (R,i), i = 1, ..., 4
”with
m(R,i)A = exp
(θi − 1)
Z 1
0
`Hτ2i−1 (u) + Hτ2i (u)
´u−ρdu
ff,
m(R,i)B = exp
8<:−R 1
0
`Hτ2i−1 (u)− Hτ2i (u)
´ “u−(ρ+β) − u−ρ
”du
βR 1
0
`Hτ2i−1 (u)− Hτ2i (u)
´u−ρdu
9=;,v (R,i) = exp
( R 1
0
`Hτ2i−1 (u)− Hτ2i (u)
´duR 1
0
`Hτ2i−1 (u)− Hτ2i (u)
´u−ρdu
).
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
Link with our framework
Suppose the third order condition and (C1) hold.
Since ϕ(R)δ is bjective and differentiable in ρ then
if the sequence k satisfies
k →∞, n/k →∞, k1/2A(n/k)→∞,
k1/2A2(n/k)→ λA and k1/2A(n/k)B(n/k)→ λB ,
we have
k1/2A(n/k)“ρ(R)
n − ρ”D−→ N
„λA
2γAB(R)
A (δ, ρ)− λBAB(R)B (δ, ρ, β),AV(R)(δ, ρ)
«with
AB(R)A (δ, ρ) =
ϕ(R)δ (ρ)
[ϕ(R)δ ]′(ρ)
log ψδ(m(R)A ), AB(R)
A (δ, ρ, β) =ϕ
(R)δ (ρ)
[ϕ(R)δ ]′(ρ)
log ψδ(m(R)B )
and
AV (R)(δ, ρ) =γϕ
(R)δ (ρ)
[ϕ(R)δ ]′(ρ)
Z 1
0
log2 ϕ(R)δ (v (R)(u))du
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
Exemples
Hτi = τiuτi−1, i = 1, ..., 8, τi > 1
New estimators of ρ (explicit or not), with Consistency and
Asymptotic normality (consequence of ours theorems)
Examples of explicit estimators
� δ = 1 i.e. θ1 − θ2 = θ3 − θ4. The rv Z(R)n is denoted by Z
(R)n,1 .
ρ(R)n,1 =
τ5ω(1, θ)− τ1Z (R)n,1
ω(1, θ)− Z(R)n,1
1l{Z (R)n,1 ∈ ω(1, θ) • (1, eψ1(τ4, τ1, τ4, τ5))}.
� δ = 0 i.e. θ1 = θ2. The rv Z(R)n is thus denoted by Z
(R)n,2 :
ρ(R)n,2 =
τ4ω(0, θ)− τ1Z (R)n,2
ω(0, θ)− Z(R)n,2
1l{Z (R)n,2 ∈ ω(0, θ) • (1, eψ0(τ4, τ1, τ4, τ5))}.
with ω(δ, θ) = eψδ(θ1(τ1 − τ2), θ2(τ2 − τ4), θ2(τ2 − τ4), θ4(τ6 − τ4))
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Link with existing estimators
2. Estimators based on the log-excesses : log Xn−j+1 − log Xn−k
Sk(τ, α) =1
k
kXj=1
Gτ,α
„j
k + 1
«„log
Xn−j+1,n
Xn−k,n
«α, α > 0,
Gτ,α is a positive function.
In the particular case where Gτ,α is constant, this statistic is used byDekkers et al. (Annals of statistics, 1989 ) to estimate γ and
by Fraga et al. . (Portugaliae Mathematica, 2003 ) to estimate
the second order parameter ρ
Ciuperca and Mercadier (Extremes, 2010 ) used the general
statistic to estimate the parameters γ and ρ.
They proved the asymptotic normality under a technical
condition on the function Gτ,α, denoted by (C2) hereafter.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
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Suppose the third order condition, (C2) hold. If the sequence ksatisfies
k →∞, n/k →∞, k1/2A(n/k)→∞,
k1/2A2(n/k)→ λA and k1/2A(n/k)B(n/k)→ λB ,
then the random vector
T (S)n =
„Sk(τi , αi )
γαi
«θi
, i = 1, ..., d
!
satisfies the model i.e. ω−1n (T
(S)n − χnI)
P−→ f (S)(ρ) with χn = 1,ωn = A(n/k)/γ(1 + oP (1)), and
f (S)(ρ) =
„−θiαi
Z 1
0
Gτi ,αi (u)(log(1/u))αi−1K−ρ(u)du; i = 1, . . . , d
«,
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d = 8, the function ψδ and ψ are the same as above
24 free parameters : {(θi , τi , αi ) ∈ (0,∞)3, i = 1, . . . , 8}Let (ζ1, . . . , ζ4) ∈ (0,∞)4 with ζ3 6= ζ4, such that
{θiαi = ζdi/2e, i = 1, . . . , 8} with δ = (ζ1 − ζ2)/(ζ3 − ζ4).
dxe = inf{n ∈ N|x ≤ n}.(τ2i−1, α2i−1) 6= (τ2i , α2i ), for i = 1, . . . , 4 and,
for i = 3, 4, (τ2i−1, α2i−1) ≤ (τ2i , α2i ) where
(x , y) 6= (s, t) means that x 6= s and/or y 6= t and (x , y) ≤ (s, t) means
that x ≤ s and y ≤ t.
T(S)n involves 20 free parameters. Z
(S)n = ψδ(T
(S)n ) and
ϕ(S)δ = ψδ ◦ f (S).
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Z(S)n does not depend on the unknown parameter.
We can thus define the following family of estimators :
ρ(S)n = ϕ−1
δ 1l{Z (S)n ∈ JS}.
Using third order condition and (C2), since ϕ(S)δ
is bijective then ρ(S)n is asymptotically Gaussian
(consequence of our theorem)
exemple of weighted function
Consider the weighted function Gτ,α is given defined by :
Gτ,α(u) =gτ−1(u)R 1
0gτ−1(x)(− log x)αdx
, τ ≥ 0, α > 0
where g0(x) = 1 and gτ−1(x) = (τ)(1− xτ−1)/(τ − 1) for τ > 1.
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exemples of estimators of ρ
New estimators of ρ (not necessarily explicit) with
Consistency and Asymptotic normality.
Exemples of explicit estimators
� δ = 0 (i.e. ζ1 = ζ2), α1 = α2 = α3 = α4 = 1, τ1 = α5 = α8 = 2
τ4 = α6 = 3. Denoting by Z(S)n,4 the rv Z
(S)n , the estimator of ρ
is given by :
ρ(S)n,4 =
6(Z(S)n,4 + 2)
3Z(S)n,4 + 4
1l{Z (S)n,4 ∈ (−2,−4/3)}.
Consider ω∗, a function depends only on δ and ζ
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Exemples of explicit estimators
� δ = 0, α1 = α3 = α4 = 1, τ1 = τ4 = α2 = α5 = α8 = 2 and α6 = 3.Denoting by Z
(S)n,5 the rv Z
(S)n ,
ρ(S)n,5 =
2(Z(S)n,5 − 2)
2Z(S)n,5 − 1
1l{Z (S)n,5 ∈ (1/2, 2)}.
ρ(S)n,4 and ρ
(S)n,5 are estimators Ciuperca and Mercadier , (Extremes,
2010).
� δ = 1, α1 = α3 = α4 = 1, τ1 = τ4 = α2 = α5 = α8 = 2 and α6 = 3Z
(S)n,6 the rv Z
(S)n , a new estimator of ρ is given by
ρ(S)n,6 =
3Z(S)n,6 − 4ω∗(1, ζ)
Z(S)n,8 − ω∗(1, ζ)
1l{Z (S)n,6 ∈ ω
∗(1, ζ) • (1/2, 2/3)}.
� δ = 1 (i.e. ζ1 − ζ2 = ζ3 − ζ4), α1 = α2 = α3 = α4 = 1, τ1 = α5 = α8 = 2 and
τ4 = α6 = 3 denoting by Z(S)n,7 the rv Z
(S)n , a nother new estimator of
ρ is given by :
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Exemples of explicit estimators
ρ(S)n,7 =
Z(S)n,7 + 4/3ω∗(1, ζ)
2Z(S)n,7 + 4/3ω∗(1, ζ)
1l{Z (S)n,7 ∈ ω
∗(1, ζ) • (−4/3,−2/3)}.
� δ = 1 (i.e. ζ1 − ζ2 = ζ3 − ζ4), α1 = α3 = α4 = 1,
τ1 = τ4 = α2 = α5 = α8 = 2 and α6 = 3 denoting by Z(S)n,8 the rv Z
(S)n , we
obtain a new estimator of ρ
ρ(S)n,8 =
3Z(S)n,8 − 4ω∗(1, ζ)
Z(S)n,8 − ω∗(1, ζ)
1l{Z (S)n,8 ∈ ω
∗(1, ζ) • (1/2, 2/3)}.
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Asymptotic comparaison
Choice of the parameters
We use here the estimator of ρ based on rescaled
log-spacings.
Hτi (u) = (τi )uτi−1, i = 1, ..., 8, τi > 1.
τ1 = 1.25, τ2 = τ3 = 1.75, τ4 = τ8 = 2, τ5 = 1.5, τ6 = τ7 = 1.75 and
θ1 = 0.01, θ3 = 0.02 θ4 = 0.04 and θ2 = θ1 + δ(θ4 − θ3), δ ≥ 0.
How to choose δ ?,
1 minimization of the AMSE is impossible (depends on unknownparameters).
2 We use an upper bound on the AMSE :AMSE ≤ c(γ, λA, λB)π(δ, ρ, β).
3 ρ = β, we minimize the function π in delta and the optimal δ asa function of ρ.
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Asymptotic comparaison
Choice of δ
−8 −6 −4 −2 0
02
46
810
ρ
δ
δ=1.5δ =1.8
Fig.: Optimal δ as a function of ρ
δ = 0, 1, 1.5, 1.8,+∞
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Illustration on a Burr distribution
Survival function of Burr distribution
Burr(ζ,λ,η) :
1− F (x) = (ζ/(ζ + xη))λ , x > 0, ζ, λ, η > 0,
is of heavy-tailed model with γ = 1/λη
Satisfies the third order condition with ρ = −1/λ and β = ρ,
A(x) = γxρ/(1− xρ) and B(x) = ρxρ/(1− xρ).
n = 5000, γ = ζ = 1, η = 1/λ, ρ = −η and k = 1, ..., 4995,
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0 1000 2000 3000 4000 5000
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ρ = − 0.25
k
AM
SE
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
0 1000 2000 3000 4000 5000
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ρ = − 1
kA
MS
E
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
Fig.: Asymptotic mean squared error of ρ(R)n , ρ = −0.25;−1
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0 1000 2000 3000 4000 5000
0.0
0.5
1.0
1.5
2.0
ρ = − 2.5
k
AM
SE
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
0 1000 2000 3000 4000 5000
0.0
0.5
1.0
1.5
2.0
2.5
ρ = − 3
kA
MS
E
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
Fig.: Asymptotic mean squared error of ρ(R)n , ρ = −2.5;−3
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0 1000 2000 3000 4000 5000
02
46
810
ρ = − 4
k
AM
SE
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
0 1000 2000 3000 4000 5000
05
1015
20
ρ = − 5
kA
MS
E
δ = 0δ = 1δ = 1.5δ = 1.8δ = + ∞
Fig.: Asymptotic mean squared error of ρ(R)n , ρ = −4;−5
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Concluding Remarks
If ρ ≤ −4, the smallest AMSE is obtained with δ = 1.8.
If −3 ≤ ρ ≤ −2.5, the best AMSE is given by δ = +∞.
If ρ ≥ −1, the smallest AMSE is given by δ = 1.5.
� The values {1.5, 1.8,+∞} obtained by minimizing the function πare also of interest to minimize the asymptotic mean-squared
error.
� More generally, the minimization of π should permit to
determine optimal values for the parameters of any estimator of
ρ.
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Outline Extreme-Value Theory New family of estimators for the second order parameter Asymptotic properties Link with existing estimators Numerical results
Main references
G. Ciuperca and C. Mercadier. Semi-parametric estimation for heavy taileddistributions. Extremes, 13, 55–87, 2010.
A.L.M. Dekkers, J.H.J. Einmahl, and L. de Haan. A moment estimator forthe index of an extreme-value distribution. Annals of Statistics, 17,1833–1855, 1989.
M.I. Fraga Alves, M.I. Gomes, and L. de Haan. A new class ofsemi-parametric estimators of the second order parameter. PortugaliaeMathematica, 60(2), 193–213, 2003.
Y. Goegebeur, J. Beirlant, and T. de Wet. Kernel estimators for thesecond order parameter in extreme value statistics. Journal of StatisticalPlanning and Inference, 140, 2632–2652, 2010.
B.M. Hill. A simple general approach to inference about the tail of adistribution, Annals of Statistics, 3, 1163–1174, 1975.
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