# Two Bootstrap Strategies for ak-Problem up to Location-Scale with Dependent Samples

Authors: Jean-François Quessy; François Éthier
Journal: Journal of Probability and Statistics
 (2014)

---

## Abstract

This paper extends the work of Quessy and Éthier (2012) who considered tests for thek-sample problem with dependent samples. Here, the marginal distributions are allowed, under H
0, to differ according to their mean and their variance; in other words, one focuses on the shape of the distributions. Although easily stated, this problem nevertheless requires a careful treatment for the computation of valid P values. To this end, two bootstrap strategies based on the multiplier central limit theorem are proposed, both exploiting a representation of the test statistics in terms of a Hadamard differentiable functional. This accounts for the fact that one works with empirically standardized data instead of the original observations. Simulations reported show the nice sample properties of the method based on Cramér-von Mises and characteristic function type statistics. The newly introduced tests are illustrated on the marginal distributions of the eight-dimensional Oil currency data set.


---

## Body

## 1. Introduction

Testing for the equality in distribution of two or more real-valued random variables is a classical statistical problem that has been extensively investigated in the literature. Formally, the goal is to test forH
0
:
F
1
=
⋯
=
F
K, where F
k
(
x
)
=
P
(
X
k
≤
x
), k
∈
{
1
,
…
,
K
}, and X
1
,
…
,
X
K are usually assumed to be independent random variables. In the bivariate case (K
=
2), the most popular procedures for H
0 are those based on the sign, Wilcoxon rank-sum, Kolmogorov-Smirnov, and Cramér-von Mises statistics. The generalization to the k-sample situation has been considered by Kiefer [1] and Bickel [2] using Kolmogorov-Smirnov and Cramér-von Mises statistics and by Scholz and Stephens [3] using the Anderson-Darling functional. More recent contributions include Zhang and Wu [4], Martínez-Camblor and de Uña-Álvarez [5], Martínez-Camblor [6], and Wyłupek [7], among others.WhenX
1
,
…
,
X
K cannot be taken as independent, the testing of H
0 must be handled with care. In that case, one knows from a celebrated theorem of Sklar [8] that there exists a copula C
:
[
0,1
]
K
→
[
0,1
] such that the joint distribution of X
=
(
X
1
,
…
,
X
K
) can be written as P
(
X
≤
x
)
=
C
{
F
1
(
x
1
)
,
…
,
F
K
(
x
K
)
}, where x
=
(
x
1
,
…
,
x
K
). If the marginal distributions F
1
,
…
,
F
K are continuous, then C is unique; see Nelsen [9] for details. As underlined by Quessy and Éthier [10], the possible asymmetry of C invalidates the use of permutation methods. An alternative statistical procedure where P values are estimated from an adapted version of the multiplier bootstrap method was proposed by these authors.The aim of this paper is to extend the test procedures of Quessy and Éthier [10] to a version of the k-sample problem when dependent random variables are rescaled with respect to their mean and variance. More specifically, consider K random variables X
1
,
…
,
X
K and let μ
k
=
E
(
X
k
) and σ
k
=
std
(
X
k
) be unknown. The null hypothesis of interest in this work is
(1)
H
0
⋆
:
F
~
1
=
⋯
=
F
~
K
,
where F
~
k
(
x
)
=
P
(
X
~
k
≤
x
) and X
~
k
=
(
X
k
-
μ
k
)
/
σ
k. In other words, one wants to test the shape hypothesis that K distributions are equalup to location and scale factors. The main interest behind this hypothesis is that the actual difference between distributions can only be an artefact caused by measures made on different scales.Because the means and variances are unknown, this problem requires a careful investigation at the level of the computation of validP values. Indeed, the fact that these moments must be estimated from the data has an impact on the limiting distribution of the test statistics. Hence, a naive approach treating μ
1
,
…
,
μ
K and σ
1
,
…
,
σ
K as known values would not work here. As will be seen, the empirical process from which the test statistics are computed has a useful representation in terms of a Hadamard differentiable functional. This is at the heart of the two bootstrap strategies that are developed in this work. The test statistics that are proposed are based on Cramér-von Mises and characteristic function mappings. It will be seen that the latter entail powerful procedures under many kinds of alternatives. Moreover, the nature of these functionals yields simple expressions in terms of quadratic forms both for their sample version and for their bootstrap counterparts.The paper is structured as follows. The Hadamard differentiable functional which is at the basis of the proposed methodologies is introduced in Section2 together with the test statistics. In Section 3, two resampling methods based on the multiplier bootstrap method are proposed and their impact on the computation of the P values of the test statistics is described. The sample properties of the newly introduced tests are studied in Section 4 with the help of Monte-Carlo simulations. An illustration on the eight-dimensionalOil currency data set is detailed in Section 5.


## 2. Test Statistics

### 2.1. Empirical Process of the Rescaled Observations

LetX
1
,
…
,
X
n be independent copies of  X
=
(
X
1
,
…
,
X
K
), where X
i
=
(
X
i
1
,
…
,
X
i
K
). For each k
∈
{
1
,
…
,
K
}, the random variable X
k is assumed to have a finite moment of order two. The estimation of the rescaled distribution functions F
~
1
,
…
,
F
~
K will be based on the empirically standardized observations
(2)
X
~
i
k
=
X
i
k
-
μ
^
k
σ
^
k
,
where, for each k
∈
{
1
,
…
,
K
}, μ
^
k and σ
^
k are the usual empirical mean and variance based on X
1
k
,
…
,
X
n
k. Specifically, the univariate distribution function F
~
k will be estimated for x
∈
R by
(3)
F
~
n
k
(
x
)
=
1
n
∑
i
=
1
n
‍
I
(
X
~
i
k
≤
x
)
.
If F
n
1
,
…
,
F
n
K denote the usual empirical distribution functions, one obtains easily F
~
n
k
(
x
)
=
F
n
k
(
σ
^
k
x
+
μ
^
k
). Letting F
~
n
=
(
F
~
n
1
,
…
,
F
~
n
K
)
⊤, one then has
(4)
F
~
n
(
x
)
=
(
F
n
1
(
σ
^
1
x
+
μ
^
1
)
,
…
,
F
n
K
(
σ
^
K
x
+
μ
^
K
)
)
⊤
.Remark 1.
The estimation ofF
~
1
,
…
,
F
~
K from empirically standardized observations is somewhat similar to the estimation of a quantile function as considered by Parzen [11]. Under the null hypothesis that a random sample X
1
,
…
,
X
n comes from a distribution F of the form F
(
x
)
=
F
~
{
(
x
-
μ
)
/
σ
} from some fixed distribution function F
~, empirically standardized observations are defined as
(5)
X
~
i
=
F
~
(
X
i
-
μ
^
σ
^
)
.
Under the null hypothesis, X
~
1
,
…
,
X
~
n are approximately uniform on [
0,1
], which motivated Parzen [11] to compare their empirical quantile function to the uniform quantile function as a goodness-of-fit criteria.The large-sample behavior ofF
~
n
=
n
(
F
~
n
-
F
~
), where F
~
=
(
F
~
1
,
…
,
F
~
K
)
⊤, will be a consequence of the Hadamard differentiability of the functional
(6)
Υ
(
F
)
(
x
)
=
(
F
1
{
Υ
σ
(
F
1
)
x
+
Υ
μ
(
F
1
)
}
,
…
,
F
K
{
Υ
σ
(
F
K
)
x
+
Υ
μ
(
F
K
)
}
)
⊤
defined on the product space of K-dimensional vectors F
=
(
F
1
,
…
,
F
K
)
⊤ of bounded functions on R. Here, for a given univariate distribution function F whose moment of order two exists, the functionals
(7)
Υ
μ
(
F
)
=
∫
0
∞
‍
{
1
-
F
(
x
)
-
F
(
-
x
)
}
d
x
,
{
Υ
σ
(
F
)
}
2
=
Υ
σ
2
(
F
)
=
2
∫
0
∞
‍
x
{
1
-
F
(
x
)
+
F
(
-
x
)
}
d
x
-
{
Υ
μ
(
F
)
}
2
are, respectively, the mean and the variance of the probability distribution associated to F. Note that, for each k
∈
{
1
,
…
,
K
},
(8)
μ
^
k
=
Υ
μ
(
F
n
k
)
,
σ
^
k
=
n
n
+
1
Υ
σ
(
F
n
k
)
≈
Υ
σ
(
F
n
k
)
.
The next result establishes that Υ is Hadamard differentiable. Recall that a map Λ
:
D
~
1
→
D
2, where D
~
1
⊆
D
1 and D
1, D
2 are normed spaces, is said to be Hadamard differentiable if there exists a continuous linear mapping Λ
δ
′
:
D
1
→
D
2, called the derivative of Λ at δ
∈
D
1, such that, for t
n
→
0 and Δ
n
→
Δ,
(9)
lim
⁡
n
→
∞
∥
Λ
(
δ
+
t
n
Δ
n
)
-
Λ
(
δ
)
t
n
-
Λ
δ
′
(
Δ
)
∥
=
0
.
If Λ
δ
′ exists only on a subset D
1
⋆ of D
1, then Λ is said to be differentiabletangentially to
D
1
⋆. See van der vaart and Wellner [12] for more details.Proposition 2.
Suppose thatF
1
,
…
,
F
K have bounded densities given, respectively, by f
1
,
…
,
f
K. Then the functional Υ is Hadamard differentiable with derivative at F
=
(
F
1
,
…
,
F
K
)
⊤ evaluated at Δ
=
(
Δ
1
,
…
,
Δ
K
)
⊤ given by
(10)
Υ
F
′
(
Δ
)
(
x
)
=
Δ
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
Δ
~
(
s
)
d
s
,
where Δ
~
=
(
Δ
~
1
,
…
,
Δ
~
K
)
⊤ with Δ
~
k
(
x
)
=
Δ
k
(
σ
k
x
+
μ
k
) and f
~
(
x
) is a K
×
K diagonal matrix with f
~
(
x
)
k
k
=
f
~
k
(
x
)
=
σ
k
f
k
(
σ
k
x
+
μ
k
),  k
∈
{
1
,
…
,
K
}.Suppose thatX has continuous marginal distributions F
1
,
…
,
F
K and let C be the unique copula associated to its joint distribution. From classical arguments, the vector of empirical processes F
n
=
n
(
F
n
-
F
) converges weakly to F
=
(
B
1
∘
F
1
,
…
,
B
K
∘
F
K
)
⊤, where B
1
,
…
,
B
K are dependent Brownian bridges such that, for s
,
t
∈
[
0,1
] and any k
,
k
′
∈
{
1
,
…
,
K
},
(11)
Γ
k
k
′
(
s
,
t
)
=
E
{
B
k
(
s
)
B
k
′
(
t
)
}
=
C
k
k
′
(
s
,
t
)
-
s
t
,
where C
k
k
′ is the copula of (
X
k
,
X
k
′
). Hence, for any x
,
y
∈
R, the limit Gaussian process F is characterized by the covariance structure G
(
x
,
y
)
=
E
{
F
(
x
)
F
(
y
)
⊤
}, where, for any k
,
k
′
∈
{
1
,
…
,
K
}, the entries of G are
(12)
G
k
k
′
(
x
,
y
)
=
Γ
k
k
′
{
F
k
(
x
)
,
F
k
′
(
y
)
}
.
The result can be extended to the empirical process F
~
n, thanks to the Hadamard differentiability of Υ stated in Proposition 2.Proposition 3.
IfF
1
,
…
,
F
K have bounded densities f
1
,
…
,
f
K and finite moments of order two, then F
~
n converges weakly to a centered Gaussian process F
~ of the form
(13)
F
~
(
x
)
=
B
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
B
~
(
s
)
d
s
,
where B
~
=
(
B
1
∘
F
~
1
,
…
,
B
K
∘
F
~
K
) and B
1
,
…
,
B
K are Brownian bridges with covariance structure given by Γ
k
k
′. The covariance structure of F
~ is given by G
~
(
x
,
y
)
=
E
{
F
~
(
x
)
F
~
(
y
)
⊤
}, where, for any k
,
k
′
∈
{
1
,
…
,
K
},
(14)
G
~
k
k
′
(
x
,
y
)
=
Γ
k
k
′
{
F
~
k
(
x
)
,
F
~
k
′
(
y
)
}
-
f
~
k
(
x
)
∫
R
‍
(
x
s
+
1
)
Γ
k
k
′
{
F
~
k
(
s
)
,
F
~
k
′
(
y
)
}
d
s
-
f
~
k
′
(
y
)
∫
R
‍
(
y
t
+
1
)
Γ
k
k
′
{
F
~
k
(
x
)
,
F
~
k
′
(
t
)
}
d
t
+
f
~
k
(
x
)
f
~
k
′
(
y
)
∫
R
2
‍
(
x
s
+
1
)
(
y
t
+
1
)
×
Γ
k
k
′
{
F
~
k
(
s
)
,
F
~
k
′
(
t
)
}
d
t
d
s
.Proposition3 can be specified to the case when the null hypothesis H
0
⋆ holds true. In that case, there is a distribution function F
~ such that
(15)
F
(
x
)
=
(
F
~
(
x
-
μ
1
σ
1
)
,
…
,
F
~
(
x
-
μ
K
σ
K
)
)
⊤
.
The result is stated as a corollary to Proposition 3.Corollary 4.
Suppose thatF
~ has a bounded density f
~ whose moment of order two exists. Under H
0
⋆, the empirical process F
~
n converges weakly to the centered Gaussian process
(16)
F
~
(
x
)
=
B
∘
F
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
B
∘
F
~
(
s
)
d
s
whose covariance structure is given by
(17)
E
{
F
~
(
x
)
F
~
(
y
)
⊤
}
=
Γ
{
F
~
(
x
)
,
F
~
(
y
)
}
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
Γ
{
F
~
(
s
)
,
F
~
(
y
)
}
d
s
-
f
~
(
y
)
∫
R
‍
(
y
t
+
1
)
Γ
{
F
~
(
x
)
,
F
~
(
t
)
}
d
t
+
f
~
(
x
)
f
~
(
y
)
∫
R
2
‍
(
x
s
+
1
)
(
y
t
+
1
)
×
Γ
{
F
~
(
s
)
,
F
~
(
t
)
}
d
t
d
s
,
where the entries of Γ are Γ
k
k
′ for k
,
k
′
∈
{
1
,
…
,
K
}.


### 2.2. Cramér-von Mises and Characteristic Function Test Statistics

Consider the Cramér-von Mises and characteristic function type functionals(18)
L
CvM
(
F
)
=
∫
R
‍
F
(
x
)
⊤
F
(
x
)
d
x
,
L
Cf
G
(
F
)
=
∫
R
‍
Ψ
(
t
)
⊤
Ψ
(
t
)
¯
d
G
(
t
)
,
with Ψ
(
t
)
=
∫
R
‍
e
i
t
x
d
F
(
x
) being the component-wise characteristic function of F
=
(
F
1
,
…
,
F
K
), and Ψ
(
t
)
¯ is its complex conjugate. Here, d
G
(
t
)
>
0 is a weight function that gives nonnull mass at each t
∈
R. Alternate representations for L
Cf
G are given in the next lemma; the latter will prove useful in the sequel.Lemma 5.
Lettingβ
G
(
u
)
=
∫
R
‍
cos
⁡
(
t
u
)
d
G
(
t
), one has
(19)
L
C
f
G
(
F
)
=
∫
R
2
‍
β
G
(
x
-
y
)
d
F
(
x
)
⊤
d
F
(
y
)
.
Moreover, if  lim
⁡
|
u
|
→
∞
β
G
(
u
)
=
0 and lim
⁡
|
u
|
→
∞
β
G
′
(
u
)
=
0, then
(20)
L
C
f
G
(
F
)
=
-
∫
R
2
‍
F
(
x
)
⊤
F
(
y
)
β
G
′′
(
x
-
y
)
d
x
d
y
.The statistics that will be investigated in this work are(21)
S
n
=
L
CvM
(
n
O
F
~
n
)
,
T
n
G
=
L
Cf
G
(
n
O
F
~
n
)
.
Here, O
∈
R
q
×
K is a combination matrix such that O
z
=
0
q if and only if z
=
r
1
K for some r
∈
R, where 0
q
=
(
0
,
…
,
0
)
⊤
∈
R
q and 1
K
=
(
1
,
…
,
1
)
⊤
∈
R
K. The null hypothesis can then be reformulated as H
0
⋆
:
O
F
~
=
0
q. The asymptotic behavior of S
n and T
n
G under H
0
⋆ is stated in the next proposition.Proposition 6.
Suppose thatF
~ has a bounded density f
~ whose moment of order two exists. Also assume that d
G is a weight function such that ∫
R
‍
t
2
d
G
(
t
)
<
∞, lim
⁡
|
u
|
→
∞
β
G
(
u
)
=
0, and lim
⁡
|
u
|
→
∞
β
G
′
(
u
)
=
0. Then, under H
0
⋆,
(22)
S
n
⇝
S
=
L
C
v
M
(
O
F
~
)
,
T
n
G
⇝
T
G
=
L
C
f
G
(
O
F
~
)
,
where F
~ is the limit of F
~
n identified in Corollary 4.An interesting feature of the test statisticsS
n and T
n
G is that simple formulas for their computation are available in terms of product of matrices. Explicit expressions similar to those given by Quessy and Éthier [10] are described in Remarks 7 and 8.Remark 7 (explicit formula forS
n).
BecauseF
⊤
F
=
1
K
⊤
diag
⁡
(
F
F
⊤
), one can show that S
n
=
n
1
q
⊤
diag
⁡
(
O
A
O
⊤
), where the entries of A
∈
R
K
×
K are
(23)
A
k
k
′
=
∫
R
‍
F
~
n
k
(
x
)
F
~
n
k
′
(
x
)
d
x
=
lim
⁡
m
→
∞
(
m
-
1
n
2
∑
l
,
l
′
=
1
n
‍
max
⁡
⁡
(
X
~
l
k
,
X
~
l
′
k
′
)
)
.
Hence, A
=
m
1
K
1
K
⊤
-
A
~
/
n
2, where A
~
k
k
′
=
∑
l
,
l
′
=
1
n
‍
max
⁡
(
X
~
l
k
,
X
~
l
′
k
′
). Since by the assumption that O
1
K
1
K
⊤ vanishes, S
n
=
-
1
q
⊤
diag
⁡
(
O
A
~
O
⊤
)
/
n.Remark 8 (explicit formula forT
n
G).
From representation (19) for L
Cf
G,
(24)
T
n
G
=
n
∫
R
2
‍
β
G
(
x
-
y
)
(
O
d
F
~
n
(
x
)
)
⊤
(
O
d
F
~
n
(
y
)
)
=
n
∫
R
2
‍
β
G
(
x
-
y
)
1
q
⊤
diag
⁡
{
O
d
F
~
n
(
x
)
d
F
~
n
(
y
)
⊤
O
⊤
)
=
n
1
q
⊤
diag
⁡
(
O
B
O
⊤
)
,
where the entries of B
=
∫
R
2
‍
β
G
(
x
-
y
)
d
F
~
n
(
x
)
d
F
~
n
(
y
)
⊤
∈
R
K
×
K are
(25)
B
k
k
′
=
1
n
2
∑
l
,
l
′
=
1
n
β
G
(
X
~
l
k
-
X
~
l
′
k
′
)
.
‍


## 2.1. Empirical Process of the Rescaled Observations

LetX
1
,
…
,
X
n be independent copies of  X
=
(
X
1
,
…
,
X
K
), where X
i
=
(
X
i
1
,
…
,
X
i
K
). For each k
∈
{
1
,
…
,
K
}, the random variable X
k is assumed to have a finite moment of order two. The estimation of the rescaled distribution functions F
~
1
,
…
,
F
~
K will be based on the empirically standardized observations
(2)
X
~
i
k
=
X
i
k
-
μ
^
k
σ
^
k
,
where, for each k
∈
{
1
,
…
,
K
}, μ
^
k and σ
^
k are the usual empirical mean and variance based on X
1
k
,
…
,
X
n
k. Specifically, the univariate distribution function F
~
k will be estimated for x
∈
R by
(3)
F
~
n
k
(
x
)
=
1
n
∑
i
=
1
n
‍
I
(
X
~
i
k
≤
x
)
.
If F
n
1
,
…
,
F
n
K denote the usual empirical distribution functions, one obtains easily F
~
n
k
(
x
)
=
F
n
k
(
σ
^
k
x
+
μ
^
k
). Letting F
~
n
=
(
F
~
n
1
,
…
,
F
~
n
K
)
⊤, one then has
(4)
F
~
n
(
x
)
=
(
F
n
1
(
σ
^
1
x
+
μ
^
1
)
,
…
,
F
n
K
(
σ
^
K
x
+
μ
^
K
)
)
⊤
.Remark 1.
The estimation ofF
~
1
,
…
,
F
~
K from empirically standardized observations is somewhat similar to the estimation of a quantile function as considered by Parzen [11]. Under the null hypothesis that a random sample X
1
,
…
,
X
n comes from a distribution F of the form F
(
x
)
=
F
~
{
(
x
-
μ
)
/
σ
} from some fixed distribution function F
~, empirically standardized observations are defined as
(5)
X
~
i
=
F
~
(
X
i
-
μ
^
σ
^
)
.
Under the null hypothesis, X
~
1
,
…
,
X
~
n are approximately uniform on [
0,1
], which motivated Parzen [11] to compare their empirical quantile function to the uniform quantile function as a goodness-of-fit criteria.The large-sample behavior ofF
~
n
=
n
(
F
~
n
-
F
~
), where F
~
=
(
F
~
1
,
…
,
F
~
K
)
⊤, will be a consequence of the Hadamard differentiability of the functional
(6)
Υ
(
F
)
(
x
)
=
(
F
1
{
Υ
σ
(
F
1
)
x
+
Υ
μ
(
F
1
)
}
,
…
,
F
K
{
Υ
σ
(
F
K
)
x
+
Υ
μ
(
F
K
)
}
)
⊤
defined on the product space of K-dimensional vectors F
=
(
F
1
,
…
,
F
K
)
⊤ of bounded functions on R. Here, for a given univariate distribution function F whose moment of order two exists, the functionals
(7)
Υ
μ
(
F
)
=
∫
0
∞
‍
{
1
-
F
(
x
)
-
F
(
-
x
)
}
d
x
,
{
Υ
σ
(
F
)
}
2
=
Υ
σ
2
(
F
)
=
2
∫
0
∞
‍
x
{
1
-
F
(
x
)
+
F
(
-
x
)
}
d
x
-
{
Υ
μ
(
F
)
}
2
are, respectively, the mean and the variance of the probability distribution associated to F. Note that, for each k
∈
{
1
,
…
,
K
},
(8)
μ
^
k
=
Υ
μ
(
F
n
k
)
,
σ
^
k
=
n
n
+
1
Υ
σ
(
F
n
k
)
≈
Υ
σ
(
F
n
k
)
.
The next result establishes that Υ is Hadamard differentiable. Recall that a map Λ
:
D
~
1
→
D
2, where D
~
1
⊆
D
1 and D
1, D
2 are normed spaces, is said to be Hadamard differentiable if there exists a continuous linear mapping Λ
δ
′
:
D
1
→
D
2, called the derivative of Λ at δ
∈
D
1, such that, for t
n
→
0 and Δ
n
→
Δ,
(9)
lim
⁡
n
→
∞
∥
Λ
(
δ
+
t
n
Δ
n
)
-
Λ
(
δ
)
t
n
-
Λ
δ
′
(
Δ
)
∥
=
0
.
If Λ
δ
′ exists only on a subset D
1
⋆ of D
1, then Λ is said to be differentiabletangentially to
D
1
⋆. See van der vaart and Wellner [12] for more details.Proposition 2.
Suppose thatF
1
,
…
,
F
K have bounded densities given, respectively, by f
1
,
…
,
f
K. Then the functional Υ is Hadamard differentiable with derivative at F
=
(
F
1
,
…
,
F
K
)
⊤ evaluated at Δ
=
(
Δ
1
,
…
,
Δ
K
)
⊤ given by
(10)
Υ
F
′
(
Δ
)
(
x
)
=
Δ
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
Δ
~
(
s
)
d
s
,
where Δ
~
=
(
Δ
~
1
,
…
,
Δ
~
K
)
⊤ with Δ
~
k
(
x
)
=
Δ
k
(
σ
k
x
+
μ
k
) and f
~
(
x
) is a K
×
K diagonal matrix with f
~
(
x
)
k
k
=
f
~
k
(
x
)
=
σ
k
f
k
(
σ
k
x
+
μ
k
),  k
∈
{
1
,
…
,
K
}.Suppose thatX has continuous marginal distributions F
1
,
…
,
F
K and let C be the unique copula associated to its joint distribution. From classical arguments, the vector of empirical processes F
n
=
n
(
F
n
-
F
) converges weakly to F
=
(
B
1
∘
F
1
,
…
,
B
K
∘
F
K
)
⊤, where B
1
,
…
,
B
K are dependent Brownian bridges such that, for s
,
t
∈
[
0,1
] and any k
,
k
′
∈
{
1
,
…
,
K
},
(11)
Γ
k
k
′
(
s
,
t
)
=
E
{
B
k
(
s
)
B
k
′
(
t
)
}
=
C
k
k
′
(
s
,
t
)
-
s
t
,
where C
k
k
′ is the copula of (
X
k
,
X
k
′
). Hence, for any x
,
y
∈
R, the limit Gaussian process F is characterized by the covariance structure G
(
x
,
y
)
=
E
{
F
(
x
)
F
(
y
)
⊤
}, where, for any k
,
k
′
∈
{
1
,
…
,
K
}, the entries of G are
(12)
G
k
k
′
(
x
,
y
)
=
Γ
k
k
′
{
F
k
(
x
)
,
F
k
′
(
y
)
}
.
The result can be extended to the empirical process F
~
n, thanks to the Hadamard differentiability of Υ stated in Proposition 2.Proposition 3.
IfF
1
,
…
,
F
K have bounded densities f
1
,
…
,
f
K and finite moments of order two, then F
~
n converges weakly to a centered Gaussian process F
~ of the form
(13)
F
~
(
x
)
=
B
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
B
~
(
s
)
d
s
,
where B
~
=
(
B
1
∘
F
~
1
,
…
,
B
K
∘
F
~
K
) and B
1
,
…
,
B
K are Brownian bridges with covariance structure given by Γ
k
k
′. The covariance structure of F
~ is given by G
~
(
x
,
y
)
=
E
{
F
~
(
x
)
F
~
(
y
)
⊤
}, where, for any k
,
k
′
∈
{
1
,
…
,
K
},
(14)
G
~
k
k
′
(
x
,
y
)
=
Γ
k
k
′
{
F
~
k
(
x
)
,
F
~
k
′
(
y
)
}
-
f
~
k
(
x
)
∫
R
‍
(
x
s
+
1
)
Γ
k
k
′
{
F
~
k
(
s
)
,
F
~
k
′
(
y
)
}
d
s
-
f
~
k
′
(
y
)
∫
R
‍
(
y
t
+
1
)
Γ
k
k
′
{
F
~
k
(
x
)
,
F
~
k
′
(
t
)
}
d
t
+
f
~
k
(
x
)
f
~
k
′
(
y
)
∫
R
2
‍
(
x
s
+
1
)
(
y
t
+
1
)
×
Γ
k
k
′
{
F
~
k
(
s
)
,
F
~
k
′
(
t
)
}
d
t
d
s
.Proposition3 can be specified to the case when the null hypothesis H
0
⋆ holds true. In that case, there is a distribution function F
~ such that
(15)
F
(
x
)
=
(
F
~
(
x
-
μ
1
σ
1
)
,
…
,
F
~
(
x
-
μ
K
σ
K
)
)
⊤
.
The result is stated as a corollary to Proposition 3.Corollary 4.
Suppose thatF
~ has a bounded density f
~ whose moment of order two exists. Under H
0
⋆, the empirical process F
~
n converges weakly to the centered Gaussian process
(16)
F
~
(
x
)
=
B
∘
F
~
(
x
)
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
B
∘
F
~
(
s
)
d
s
whose covariance structure is given by
(17)
E
{
F
~
(
x
)
F
~
(
y
)
⊤
}
=
Γ
{
F
~
(
x
)
,
F
~
(
y
)
}
-
f
~
(
x
)
∫
R
‍
(
x
s
+
1
)
Γ
{
F
~
(
s
)
,
F
~
(
y
)
}
d
s
-
f
~
(
y
)
∫
R
‍
(
y
t
+
1
)
Γ
{
F
~
(
x
)
,
F
~
(
t
)
}
d
t
+
f
~
(
x
)
f
~
(
y
)
∫
R
2
‍
(
x
s
+
1
)
(
y
t
+
1
)
×
Γ
{
F
~
(
s
)
,
F
~
(
t
)
}
d
t
d
s
,
where the entries of Γ are Γ
k
k
′ for k
,
k
′
∈
{
1
,
…
,
K
}.


## 2.2. Cramér-von Mises and Characteristic Function Test Statistics

Consider the Cramér-von Mises and characteristic function type functionals(18)
L
CvM
(
F
)
=
∫
R
‍
F
(
x
)
⊤
F
(
x
)
d
x
,
L
Cf
G
(
F
)
=
∫
R
‍
Ψ
(
t
)
⊤
Ψ
(
t
)
¯
d
G
(
t
)
,
with Ψ
(
t
)
=
∫
R
‍
e
i
t
x
d
F
(
x
) being the component-wise characteristic function of F
=
(
F
1
,
…
,
F
K
), and Ψ
(
t
)
¯ is its complex conjugate. Here, d
G
(
t
)
>
0 is a weight function that gives nonnull mass at each t
∈
R. Alternate representations for L
Cf
G are given in the next lemma; the latter will prove useful in the sequel.Lemma 5.
Lettingβ
G
(
u
)
=
∫
R
‍
cos
⁡
(
t
u
)
d
G
(
t
), one has
(19)
L
C
f
G
(
F
)
=
∫
R
2
‍
β
G
(
x
-
y
)
d
F
(
x
)
⊤
d
F
(
y
)
.
Moreover, if  lim
⁡
|
u
|
→
∞
β
G
(
u
)
=
0 and lim
⁡
|
u
|
→
∞
β
G
′
(
u
)
=
0, then
(20)
L
C
f
G
(
F
)
=
-
∫
R
2
‍
F
(
x
)
⊤
F
(
y
)
β
G
′′
(
x
-
y
)
d
x
d
y
.The statistics that will be investigated in this work are(21)
S
n
=
L
CvM
(
n
O
F
~
n
)
,
T
n
G
=
L
Cf
G
(
n
O
F
~
n
)
.
Here, O
∈
R
q
×
K is a combination matrix such that O
z
=
0
q if and only if z
=
r
1
K for some r
∈
R, where 0
q
=
(
0
,
…
,
0
)
⊤
∈
R
q and 1
K
=
(
1
,
…
,
1
)
⊤
∈
R
K. The null hypothesis can then be reformulated as H
0
⋆
:
O
F
~
=
0
q. The asymptotic behavior of S
n and T
n
G under H
0
⋆ is stated in the next proposition.Proposition 6.
Suppose thatF
~ has a bounded density f
~ whose moment of order two exists. Also assume that d
G is a weight function such that ∫
R
‍
t
2
d
G
(
t
)
<
∞, lim
⁡
|
u
|
→
∞
β
G
(
u
)
=
0, and lim
⁡
|
u
|
→
∞
β
G
′
(
u
)
=
0. Then, under H
0
⋆,
(22)
S
n
⇝
S
=
L
C
v
M
(
O
F
~
)
,
T
n
G
⇝
T
G
=
L
C
f
G
(
O
F
~
)
,
where F
~ is the limit of F
~
n identified in Corollary 4.An interesting feature of the test statisticsS
n and T
n
G is that simple formulas for their computation are available in terms of product of matrices. Explicit expressions similar to those given by Quessy and Éthier [10] are described in Remarks 7 and 8.Remark 7 (explicit formula forS
n).
BecauseF
⊤
F
=
1
K
⊤
diag
⁡
(
F
F
⊤
), one can show that S
n
=
n
1
q
⊤
diag
⁡
(
O
A
O
⊤
), where the entries of A
∈
R
K
×
K are
(23)
A
k
k
′
=
∫
R
‍
F
~
n
k
(
x
)
F
~
n
k
′
(
x
)
d
x
=
lim
⁡
m
→
∞
(
m
-
1
n
2
∑
l
,
l
′
=
1
n
‍
max
⁡
⁡
(
X
~
l
k
,
X
~
l
′
k
′
)
)
.
Hence, A
=
m
1
K
1
K
⊤
-
A
~
/
n
2, where A
~
k
k
′
=
∑
l
,
l
′
=
1
n
‍
max
⁡
(
X
~
l
k
,
X
~
l
′
k
′
). Since by the assumption that O
1
K
1
K
⊤ vanishes, S
n
=
-
1
q
⊤
diag
⁡
(
O
A
~
O
⊤
)
/
n.Remark 8 (explicit formula forT
n
G).
From representation (19) for L
Cf
G,
(24)
T
n
G
=
n
∫
R
2
‍
β
G
(
x
-
y
)
(
O
d
F
~
n
(
x
)
)
⊤
(
O
d
F
~
n
(
y
)
)
=
n
∫
R
2
‍
β
G
(
x
-
y
)
1
q
⊤
diag
⁡
{
O
d
F
~
n
(
x
)
d
F
~
n
(
y
)
⊤
O
⊤
)
=
n
1
q
⊤
diag
⁡
(
O
B
O
⊤
)
,
where the entries of B
=
∫
R
2
‍
β
G
(
x
-
y
)
d
F
~
n
(
x
)
d
F
~
n
(
y
)
⊤
∈
R
K
×
K are
(25)
B
k
k
′
=
1
n
2
∑
l
,
l
′
=
1
n
β
G
(
X
~
l
k
-
X
~
l
′
k
′
)
.
‍


## 3. Resampling

### 3.1. BootstrappingF
n

The challenging issue of computing validP values for test statistics based on S
n and T
n
G is addressed in this section, where two bootstrap methods are developed. One version is based directly on the functional Υ, while the other one is built around its Hadamard derivative. The key element here is the so-called multiplier bootstrap method applied to the empirical process F
n. This resampling method is a very powerful technique which is especially useful when testing for composite hypotheses. In the case of F
n, one defines, for each h
∈
{
1
,
…
,
M
}, the vector of processes
(26)
F
n
(
h
)
(
x
)
=
1
n
∑
i
=
1
n
‍
(
ξ
i
(
h
)
ξ
¯
(
h
)
-
1
)
1
n
∑
i
=
1
n
‍
×
(
I
(
X
i
1
≤
x
)
,
…
,
I
(
X
i
K
≤
x
)
)
⊤
,
where ξ
(
1
)
,
…
,
ξ
(
M
), with ξ
(
h
)
=
(
ξ
1
(
h
)
,
…
,
ξ
n
(
h
)
), are independent vectors of independent random variables having unit mean and variance and such that ∫
R
‍
{
P
(
ξ
i
(
h
)
>
x
)
}
1
/
2
d
x
<
∞. This version of the multiplier method is called the Bayesian bootstrap in Kosorok [13]. Straightforward arguments given in Quessy and Éthier [10] enable establishing the weak convergence of (
F
n
,
F
n
(
1
)
,
…
,
F
n
(
M
)
) to (
F
,
F
(
1
)
,
…
,
F
(
M
)
), where F
(
1
)
,
…
,
F
(
M
) are independent copies of F. The method is extended to F
~
n in the next subsection.


### 3.2.  BootstrappingF
~
n: Two Approaches

Inspired from (10) in the special case when f
~
1
=
⋯
=
f
~
K
=
f
~, let
(27)
Υ
F
′
^
(
Δ
)
(
x
)
=
Δ
~
n
(
x
)
-
f
~
n
(
x
)
∫
R
‍
(
x
s
+
1
)
Δ
~
n
(
s
)
d
s
,
where, based on the available data, Δ
~
n
=
(
Δ
~
n
1
,
…
,
Δ
~
n
K
)
⊤ with Δ
~
n
k
(
x
)
=
Δ
k
(
σ
^
k
x
+
μ
^
k
) and f
~
n is a uniformly consistent estimator of f
~. Then, define the multiplier bootstrap versions of F
~
n by F
~
n
1
(
1
)
,
…
,
F
~
n
1
(
M
), where
(28)
F
~
n
1
(
h
)
=
Υ
F
′
^
(
F
n
(
h
)
)
.
A second multiplier bootstrap approach for F
~
n which, unlike F
~
n
1
(
h
), requires no density estimation consists in defining
(29)
F
~
n
2
(
h
)
=
n
{
Υ
(
F
n
+
F
n
(
h
)
n
)
-
Υ
(
F
n
)
}
,
for each h
∈
{
1
,
…
,
M
}. The asymptotic validity of these two resampling methods is established in the next proposition.Proposition 9.
Under the conditions of Proposition3, one has in the space l
∞
(
R
) that
(30)
(
F
~
n
,
F
~
n
b
(
1
)
,
…
,
F
~
n
b
(
M
)
)
⇝
(
F
~
,
F
~
b
(
1
)
,
…
,
F
~
b
(
M
)
)
,
b
=
1,2
,
where F
~
b
(
1
)
,
…
,
F
~
b
(
M
) are independent copies of F
~.


### 3.3. Multiplier Bootstrap Versions of the Test Statistics

For eachh
∈
{
1
,
…
,
M
}, multiplier bootstrap versions of the test statistics S
n and T
n
G are given for h
∈
{
1
,
…
,
M
} by
(31)
S
n
b
(
h
)
=
L
CvM
(
O
F
~
n
b
(
h
)
)
,
T
n
b
G
,
(
h
)
=
L
Cf
G
(
O
F
~
n
b
(
h
)
)
,
b
=
1,2
.
Explicit and easy-to-implement formulas for these multiplier statistics are given in Appendix B. The asymptotic validity of these bootstrapped statistics is a straightforward consequence of Proposition 9 and of the continuous mapping theorem. In other words, one has the weak convergence result (
S
n
,
S
n
b
(
1
)
,
…
,
S
n
b
(
M
)
)
⇝
(
S
,
S
b
(
1
)
,
…
,
S
b
(
M
)
) for b
=
1,2, and similarly for the characteristic function statistics. Hence, asymptotically valid P values for the tests based on S
n and T
n
G are given, respectively, for b
=
1,2, by
(32)
P
^
S
,
b
=
1
M
∑
h
=
1
M
‍
I
(
S
n
b
(
h
)
>
S
n
)
,
P
^
T
G
,
b
=
1
M
∑
h
=
1
M
‍
I
(
T
n
b
G
,
(
h
)
>
T
n
G
)
.
One rejects H
0
⋆ whenever P
^
S
,
b
<
α or P
^
T
G
,
b
<
α for some predetermined significance level α. These testing procedures are consistent. Indeed, on one side, if the null hypothesis fails to hold, then O
F
~
(
x
)
≠
0, for each x, in a subset of R with nonnull probability; as a consequence,
(33)
S
n
n
⟶
L
CvM
(
O
F
~
)
>
0
,
T
n
G
n
⟶
L
Cf
(
O
F
~
)
>
0
,
and then S
n
→
+
∞ and T
n
G
→
+
∞ in probability. On the other side, the weak convergence result (
S
n
,
S
n
b
(
1
)
,
…
,
S
n
b
(
M
)
)
⇝
(
S
,
S
b
(
1
)
,
…
,
S
b
(
M
)
) (and also for the characteristic function tests) still holds even under a failure of the null hypothesis so that the bootstrap replications are bounded in probability. It shows that the probability of rejection of H
0
⋆ tends to 1 as n
→
∞.


## 3.1. BootstrappingF
n

The challenging issue of computing validP values for test statistics based on S
n and T
n
G is addressed in this section, where two bootstrap methods are developed. One version is based directly on the functional Υ, while the other one is built around its Hadamard derivative. The key element here is the so-called multiplier bootstrap method applied to the empirical process F
n. This resampling method is a very powerful technique which is especially useful when testing for composite hypotheses. In the case of F
n, one defines, for each h
∈
{
1
,
…
,
M
}, the vector of processes
(26)
F
n
(
h
)
(
x
)
=
1
n
∑
i
=
1
n
‍
(
ξ
i
(
h
)
ξ
¯
(
h
)
-
1
)
1
n
∑
i
=
1
n
‍
×
(
I
(
X
i
1
≤
x
)
,
…
,
I
(
X
i
K
≤
x
)
)
⊤
,
where ξ
(
1
)
,
…
,
ξ
(
M
), with ξ
(
h
)
=
(
ξ
1
(
h
)
,
…
,
ξ
n
(
h
)
), are independent vectors of independent random variables having unit mean and variance and such that ∫
R
‍
{
P
(
ξ
i
(
h
)
>
x
)
}
1
/
2
d
x
<
∞. This version of the multiplier method is called the Bayesian bootstrap in Kosorok [13]. Straightforward arguments given in Quessy and Éthier [10] enable establishing the weak convergence of (
F
n
,
F
n
(
1
)
,
…
,
F
n
(
M
)
) to (
F
,
F
(
1
)
,
…
,
F
(
M
)
), where F
(
1
)
,
…
,
F
(
M
) are independent copies of F. The method is extended to F
~
n in the next subsection.


## 3.2.  BootstrappingF
~
n: Two Approaches

Inspired from (10) in the special case when f
~
1
=
⋯
=
f
~
K
=
f
~, let
(27)
Υ
F
′
^
(
Δ
)
(
x
)
=
Δ
~
n
(
x
)
-
f
~
n
(
x
)
∫
R
‍
(
x
s
+
1
)
Δ
~
n
(
s
)
d
s
,
where, based on the available data, Δ
~
n
=
(
Δ
~
n
1
,
…
,
Δ
~
n
K
)
⊤ with Δ
~
n
k
(
x
)
=
Δ
k
(
σ
^
k
x
+
μ
^
k
) and f
~
n is a uniformly consistent estimator of f
~. Then, define the multiplier bootstrap versions of F
~
n by F
~
n
1
(
1
)
,
…
,
F
~
n
1
(
M
), where
(28)
F
~
n
1
(
h
)
=
Υ
F
′
^
(
F
n
(
h
)
)
.
A second multiplier bootstrap approach for F
~
n which, unlike F
~
n
1
(
h
), requires no density estimation consists in defining
(29)
F
~
n
2
(
h
)
=
n
{
Υ
(
F
n
+
F
n
(
h
)
n
)
-
Υ
(
F
n
)
}
,
for each h
∈
{
1
,
…
,
M
}. The asymptotic validity of these two resampling methods is established in the next proposition.Proposition 9.
Under the conditions of Proposition3, one has in the space l
∞
(
R
) that
(30)
(
F
~
n
,
F
~
n
b
(
1
)
,
…
,
F
~
n
b
(
M
)
)
⇝
(
F
~
,
F
~
b
(
1
)
,
…
,
F
~
b
(
M
)
)
,
b
=
1,2
,
where F
~
b
(
1
)
,
…
,
F
~
b
(
M
) are independent copies of F
~.


## 3.3. Multiplier Bootstrap Versions of the Test Statistics

For eachh
∈
{
1
,
…
,
M
}, multiplier bootstrap versions of the test statistics S
n and T
n
G are given for h
∈
{
1
,
…
,
M
} by
(31)
S
n
b
(
h
)
=
L
CvM
(
O
F
~
n
b
(
h
)
)
,
T
n
b
G
,
(
h
)
=
L
Cf
G
(
O
F
~
n
b
(
h
)
)
,
b
=
1,2
.
Explicit and easy-to-implement formulas for these multiplier statistics are given in Appendix B. The asymptotic validity of these bootstrapped statistics is a straightforward consequence of Proposition 9 and of the continuous mapping theorem. In other words, one has the weak convergence result (
S
n
,
S
n
b
(
1
)
,
…
,
S
n
b
(
M
)
)
⇝
(
S
,
S
b
(
1
)
,
…
,
S
b
(
M
)
) for b
=
1,2, and similarly for the characteristic function statistics. Hence, asymptotically valid P values for the tests based on S
n and T
n
G are given, respectively, for b
=
1,2, by
(32)
P
^
S
,
b
=
1
M
∑
h
=
1
M
‍
I
(
S
n
b
(
h
)
>
S
n
)
,
P
^
T
G
,
b
=
1
M
∑
h
=
1
M
‍
I
(
T
n
b
G
,
(
h
)
>
T
n
G
)
.
One rejects H
0
⋆ whenever P
^
S
,
b
<
α or P
^
T
G
,
b
<
α for some predetermined significance level α. These testing procedures are consistent. Indeed, on one side, if the null hypothesis fails to hold, then O
F
~
(
x
)
≠
0, for each x, in a subset of R with nonnull probability; as a consequence,
(33)
S
n
n
⟶
L
CvM
(
O
F
~
)
>
0
,
T
n
G
n
⟶
L
Cf
(
O
F
~
)
>
0
,
and then S
n
→
+
∞ and T
n
G
→
+
∞ in probability. On the other side, the weak convergence result (
S
n
,
S
n
b
(
1
)
,
…
,
S
n
b
(
M
)
)
⇝
(
S
,
S
b
(
1
)
,
…
,
S
b
(
M
)
) (and also for the characteristic function tests) still holds even under a failure of the null hypothesis so that the bootstrap replications are bounded in probability. It shows that the probability of rejection of H
0
⋆ tends to 1 as n
→
∞.


## 4. Sample Properties of the Tests

The asymptotic behavior ofS
n and T
n
G as well as the asymptotic validity of two multiplier bootstrap methods has been established in the preceding sections. These results tell little, however, about the sample properties of the tests in small and moderate sample sizes. It is thus important to investigate their ability to keep their nominal level under H
0
⋆ as well as their power under various alternatives in situations when only a limited number of observations are available. This will be done here via Monte-Carlo simulations.The models considered for the marginal distributions are the Normal (N), Student with ν degrees of freedom (t
ν), and double-exponential (D
E
) distributions. In each of the scenarios under study, F
1 is the Normal distribution and F
2
=
⋯
=
F
K are either the N, t
9, t
6, t
3, or D
E distribution. Because the test statistics are invariant under location/scale transformations, it is enough to consider only thestandard version of each of these distributions, that is, when the mean and variance are set to 0 and 1, respectively.Because one expects that the dependence structure underlying the multivariate distribution has an influence on the results, symmetric and asymmetric dependence structures have been considered. They are all based on theNormal copula that one can extract from the multivariate Normal distribution. If h
R is the K-variate Normal density with correlation matrix R, then it is implicitly given for u
=
(
u
1
,
…
,
u
K
)
∈
[
0,1
]
K by
(34)
C
R
N
(
u
)
=
∫
-
∞
Φ
-
1
(
u
1
)
‍
⋯
∫
-
∞
Φ
-
1
(
u
K
)
‍
h
R
(
x
)
d
x
.
Note that unlike the resampling methods based on permutation, our procedure is valid under asymmetric joint distributions. Asymmetric versions of C
R
N obtained from Khoudraji’s device [14] are given by
(35)
K
R
,
δ
N
(
u
)
=
(
∏
k
=
1
K
‍
u
k
δ
k
)
C
R
N
(
u
1
1
-
δ
1
,
…
,
u
K
1
-
δ
K
)
,
where δ
=
(
δ
1
,
…
,
δ
K
)
∈
[
0,1
]
K. In the simulation study, only the special case when δ
=
(
1
/
2,0
,
…
,
0
) was considered, yielding the asymmetric copula model K
R
N
(
u
)
=
u
1
C
R
N
(
u
1
,
u
2
,
…
,
u
K
).For all the simulation results reported, the multiplier random variables are exponential with mean one. For the caseK
=
2, the number of bootstrap samples has been set to M
=
250; this has been reduced to M
=
100 when K
=
3
,
4 in order to speed up the simulations. For the first bootstrap strategy, the grid for the approximation has one hundred points and f
~
n is a kernel density estimator based on the whole sample of the n
×
K empirically standardized observations X
~
i
k, i
∈
{
1
,
…
,
n
}, k
∈
{
1
,
…
,
K
}; that is,
(36)
f
~
n
(
x
)
=
1
n
K
h
∑
i
=
1
n
‍
∑
k
=
1
K
‍
ω
(
x
-
X
~
i
k
h
)
,
where ∫
R
‍
ω
(
x
)
d
x
=
1. In the simulations presented, ω is the Epanechnikov kernel (see Epanechnikov [15]) and h is the optimal bandwidth for the estimation of Normal densities. For the characteristic function statistics, two weight functions have been considered. The first is d
G
1
(
t
)
=
e
-
t
2, for which β
G
1
(
u
)
=
π
e
-
u
2
/
4 and β
G
1
′′
(
u
)
=
π
e
-
u
2
/
4
(
u
2
-
2
)
/
4; the second is d
G
2
(
t
)
=
e
-
|
t
|, for which β
G
2
(
u
)
=
2
/
(
u
2
+
1
) and β
G
2
′′
(
u
)
=
(
12
u
2
-
4
)
/
(
u
2
+
1
)
3. The corresponding test statistics based on the combination matrix O
=
I
K
×
K
-
1
K
1
K
⊤
/
K are referred to as T
n
G
1 and T
n
G
2.The results for the caseK
=
2 are in Table 1 (n
=
100) and Table 2 (n
=
200). First, note that the ability of the tests to keep their 5% nominal level is quite good, except for S
n when using the second bootstrap strategy; simulations not reported here indicate that as high as n
=
1000 observations are needed in order for S
n to keep its size, as predicted from the asymptotic theory. Overall, considering the complexity of the bootstrap methods involved in the computation of P values, the results are very satisfying.Table 1
Percentages of rejection, as estimated from 1000 replicates, of the null hypothesis of the equality ofK
=
2 distributions up to location/scale under Normal and asymmetric Normal dependence structures when n
=
100; F
1 is the Normal distribution; F
2 is either the N, t
9, t
6, t
3, or D
E distribution.
Copula
F
2
Bootstrap method I
Bootstrap method II
S
n
T
n
G
1
T
n
G
2
S
n
T
n
G
1
T
n
G
2
C
N, τ
=
-
1
/
2
N
3.4
3.5
3.3
1.5
6.5
3.0
t
9
6.3
5.5
5.7
3.5
8.4
5.8
t
6
10.8
8.4
11.3
5.4
15.4
10.5
t
3
41.9
29.2
45.9
37.1
49.3
46.6
D
E
36.6
20.9
38.8
21.3
28.5
35.2
C
N, τ
=
1
/
2
N
4.6
4.8
4.9
0.4
5.6
3.4
t
9
7.4
8.7
8.0
3.1
13.1
6.6
t
6
13.2
12.6
14.7
5.1
20.9
13.1
t
3
48.6
41.1
54.0
41.8
63.8
55.9
D
E
53.6
39.5
57.1
28.7
47.3
51.9
K
N, τ
=
-
1
/
2
N
4.9
4.4
5.2
1.5
5.9
4.3
t
9
6.8
5.3
7.6
2.4
8.3
6.9
t
6
10.8
9.2
12.1
5.8
14.9
10.6
t
3
37.6
26.8
40.1
33.1
48.4
44.0
D
E
38.4
21.6
40.4
22.7
30.6
35.1
K
N, τ
=
1
/
2
N
4.1
4.5
5.2
0.7
5.2
2.9
t
9
6.5
7.2
7.5
1.7
10.4
5.8
t
6
13.4
13.0
15.5
6.2
21.5
14.1
t
3
49.7
40.9
54.2
42.9
61.3
57.0
D
E
48.8
35.2
51.3
27.5
44.0
45.5Table 2
Percentages of rejection, as estimated from 1000 replicates, of the null hypothesis of the equality ofK
=
2 distributions up to location/scale under Normal and asymmetric Normal dependence structures when n
=
200; F
1 is the Normal distribution; F
2 is either the N, t
9, t
6, t
3, or D
E distribution.
Copula
F
2
Bootstrap method I
Bootstrap method II
S
n
T
n
G
1
T
n
G
2
S
n
T
n
G
1
T
n
G
2
C
N, τ
=
-
1
/
2
N
5.3
4.0
5.5
2.9
4.7
5.6
t
9
9.5
8.7
10.9
5.6
10.7
10.6
t
6
17.3
13.8
20.5
12.9
21.1
21.0
t
3
76.1
64.2
78.6
75.0
80.3
82.4
D
E
75.0
51.6
79.0
64.7
58.3
76.0
C
N, τ
=
1
/
2
N
4.3
4.0
5.5
0.9
3.8
3.7
t
9
11.2
13.3
13.4
6.0
17.7
13.3
t
6
28.0
27.1
32.3
19.2
37.7
33.1
t
3
85.6
76.7
86.5
84.8
91.2
90.0
D
E
87.4
77.4
86.6
77.0
80.9
86.0
K
N, τ
=
-
1
/
2
N
3.9
4.2
4.0
2.1
5.1
4.4
t
9
10.0
7.6
10.2
6.3
11.0
9.5
t
6
20.9
16.1
20.0
13.9
22.3
19.7
t
3
74.5
64.0
80.2
76.0
82.1
83.1
D
E
74.0
52.8
77.5
63.4
58.9
75.0
K
N, τ
=
1
/
2
N
4.9
4.7
4.6
2.0
5.2
3.7
t
9
9.9
10.9
13.8
5.8
14.5
12.8
t
6
25.1
24.1
29.8
17.1
33.7
30.2
t
3
79.8
72.5
84.5
81.2
88.6
88.6
D
E
84.3
72.2
87.7
75.4
78.2
86.7The tests are also very good at rejecting the null hypothesis in case of departures from the equality of standardized distributions. Of course, the probability of rejection is higher whenn
=
200 than when n
=
100, as expected from the consistency of the tests. Also, the Normal distribution is well distinguished from the Student distribution t
ν when ν is low; it is also the case of the double-exponential distribution. Note that the observed powers are larger for a high level of dependence, that is, τ
=
1
/
2, compared to low level of dependence, that is, τ
=
-
1
/
2. However, for a given level of dependence, the asymmetric and symmetric dependence structure yielded similar results.The bootstrap method that is use in conjunction with a test statistic, has a significant influence on the power results. ForS
n, it is when the first bootstrap is used that the best powers are observed; the opposite comment applies to T
n
G
1. For T
n
G
2, the results are similar for both resampling methods. Generally speaking, the best test statistic when using the first bootstrap method is T
n
G
2, which is slightly better than S
n; T
n
G
1 is markedly less powerful. For the second bootstrap, the results vary with respect to the underlying dependence structure. Indeed, under Student alternatives, T
n
G
1 and T
n
G
2 have similar power, and they are significantly more powerful than S
n; under a double-exponential alternative, it is T
n
G
2 which is significantly more powerful than its two competitors.The results forK
=
3 and K
=
4 are presented, respectively, in Tables 3 and 4. One can see that the comments for the case K
=
2 are still valid here. Hence, globally, one can say that the newly introduced test statistics and the two bootstrap methods yield very reliable statistical procedures: they keep their nominal level well under moderate sample sizes and they are powerful under a large variety of alternatives. These properties are well maintained under various dependence structures, including symmetric and asymmetric copulas, as well as negative and positive dependence.Table 3
Percentages of rejection, as estimated from 1000 replicates, of the null hypothesis of the equality ofK
=
3 distributions up to location/scale under Normal and asymmetric Normal dependence structures when n
=
200; F
1 is the Normal distribution; F
2
=
F
3 are either the N, t
9, t
6, t
3, or D
E distribution.
Copula
F
2
=
F
3
Bootstrap method I
Bootstrap method II
S
n
T
n
G
1
T
n
G
2
S
n
T
n
G
1
T
n
G
2
C
N, τ
=
1
/
3
N
4.2
4.3
4.6
0.9
5.1
3.0
t
9
9.3
7.6
12.1
4.0
11.9
10.6
t
6
20.0
14.0
23.2
10.5
25.5
22.1
t
3
60.2
43.4
79.7
74.6
82.4
87.3
D
E
75.1
45.3
79.7
58.8
64.3
79.1
C
N, τ
=
2
/
3
N
3.5
3.6
4.9
0.7
4.6
3.6
t
9
12.5
15.3
17.7
2.3
23.5
16.0
t
6
30.9
30.8
39.5
13.5
50.5
40.4
t
3
85.5
71.2
94.0
91.0
97.4
98.1
D
E
96.0
88.9
97.1
85.8
95.7
97.6
K
N, τ
=
1
/
3
N
4.7
3.8
4.9
0.8
5.8
4.5
t
9
9.1
8.2
10.7
2.9
11.9
11.0
t
6
20.3
17.9
20.5
14.1
29.1
21.9
t
3
64.4
49.7
69.5
74.5
85.5
84.0
D
E
75.9
54.2
79.3
63.7
71.0
80.2
K
N, τ
=
2
/
3
N
3.4
5.5
4.0
0.9
5.7
2.5
t
9
11.8
14.6
14.6
3.7
17.2
13.2
t
6
22.6
25.7
28.7
11.9
37.9
29.6
t
3
81.1
73.8
86.6
80.3
91.9
91.8
D
E
84.2
74.7
85.6
69.3
80.5
85.5Table 4
Percentages of rejection, as estimated from 1000 replicates, of the null hypothesis of the equality ofK
=
4 distributions up to location/scale under Normal and asymmetric Normal dependence structures when n
=
200; F
1 is the Normal distribution; F
2
=
F
3
=
F
4 are either the N, t
9, t
6, t
3, or D
E distribution.
Copula
F
2
=
F
3
=
F
4
Bootstrap method I
Bootstrap method II
S
n
T
n
G
1
T
n
G
2
S
n
T
n
G
1
T
n
G
2
C
N, τ
=
1
/
3
N
3.8
3.7
3.6
0.7
5.7
2.8
t
9
6.7
6.4
10.7
1.9
11.7
8.8
t
6
12.2
8.4
20.6
7.4
21.0
20.6
t
3
51.4
31.6
74.9
69.2
81.5
84.7
D
E
65.9
35.5
80.8
50.9
57.3
80.9
C
N, τ
=
2
/
3
N
3.5
3.2
3.5
0.0
4.2
2.8
t
9
9.4
10.4
15.1
1.3
20.5
12.8
t
6
27.8
23.4
36.0
6.4
48.7
36.2
t
3
76.0
58.6
91.0
84.9
96.3
96.4
D
E
94.0
81.1
97.3
76.9
93.7
97.3
K
N, τ
=
1
/
3
N
3.8
3.4
5.0
0.3
5.2
3.5
t
9
7.4
6.6
12.1
1.3
12.7
12.2
t
6
15.1
11.5
17.8
7.7
26.5
19.7
t
3
55.5
34.8
59.9
73.2
85.1
81.7
D
E
69.6
46.7
74.9
54.0
66.8
75.3
K
N, τ
=
2
/
3
N
4.3
4.4
4.7
0.4
5.0
3.7
t
9
9.7
11.3
13.5
2.5
16.4
13.1
t
6
24.6
25.6
26.4
9.2
35.0
25.9
t
3
80.8
74.3
82.6
78.1
91.7
89.1
D
E
83.3
75.2
86.2
63.8
82.2
86.7


## 5. Illustration on theOil Currency Data Set

This data set consists of daily log-returns of the oil price(
X
1
), Standard and Poor’s 500 (
X
2
), and six currency exchange rates, namely, those of Great Britain pound (X
3), United States dollar (X
4), Swiss franc (X
5), Japanese yen (X
6), Danish krone (X
7), and Swedish krona (X
8) registered from May 1985 to June 2004. It has been analyzed by Kluppelberg and Kuhn [16] to illustrate their newly introducedcopula structure analysis. With a goodness-of-fit test specifically designed for the metaelliptical families of distributions, Quessy and Bellerive [17] concluded that a Student copula with an estimated sixteen degrees of freedom was suitable for these data when considering the last n
=
904 observations.The tests based onS
n, T
n
G
1, and T
n
G
2 for the equality of the standardized distributions will now be illustrated on the twenty-eight possible pairs; the results can be found in Table 5. For each of these three test statistics, their associated P value has been estimated from M
=
250 multiplier bootstrap samples from each of the two bootstrap strategies that have been proposed; these resampling methods are referred to as PV I and PV II in Table 5. Overall, all tests are generally in agreement on the acceptance or rejection of the null hypothesis, whatever the bootstrap method that was employed is.Table 5
Data analysis of each pair in the eight-dimensionalOil currency dataset: value of the Cramér-von Mises statistic S
n and of the characteristic function statistics T
n
G
1 and T
n
G
2, along with estimated P values from M
=
250 replicates of the two bootstrap strategies.
Pair
S
n
PV I
PV II
T
n
G
1
PV I
PV II
T
n
G
2
PV I
PV II
(1, 2)
0.364
0.188
0.404
0.208
0.332
0.384
0.495
0.476
0.480
(1, 3)
0.682
0.032
0.032
0.572
0.052
0.052
1.795
0.024
0.016
(1, 4)
0.550
0.036
0.076
0.509
0.032
0.052
1.355
0.040
0.064
(1, 5)
0.351
0.376
0.412
0.229
0.464
0.448
0.789
0.392
0.324
(1, 6)
0.131
0.856
0.960
0.097
0.648
0.548
0.179
0.872
0.876
(1, 7)
4.180
0.088
0.056
6.531
0.064
0.028
10.089
0.088
0.072
(1, 8)
0.271
0.492
0.496
0.180
0.484
0.396
0.601
0.460
0.388
(2, 3)
0.900
0.004
0.008
0.336
0.100
0.100
2.200
0.004
0.008
(2, 4)
1.010
<0.004
<0.004
0.553
0.008
0.012
2.181
0.008
<0.004
(2, 5)
0.355
0.408
0.488
0.295
0.384
0.304
0.520
0.584
0.556
(2, 6)
0.403
0.124
0.288
0.181
0.352
0.312
0.574
0.400
0.368
(2, 7)
4.205
0.108
0.068
6.555
0.088
0.020
9.500
0.116
0.092
(2, 8)
0.422
0.120
0.236
0.113
0.500
0.564
0.724
0.232
0.256
(3, 4)
0.184
0.440
0.672
0.173
0.176
0.116
0.378
0.372
0.432
(3, 5)
1.245
0.028
0.024
1.037
0.084
0.052
3.427
0.020
0.016
(3, 6)
0.432
0.036
0.068
0.307
0.048
0.056
1.207
0.032
0.032
(3, 7)
6.114
0.044
0.040
9.053
0.032
0.008
15.593
0.044
0.048
(3, 8)
0.242
0.372
0.516
0.140
0.404
0.404
0.644
0.260
0.248
(4, 5)
1.278
0.016
0.012
1.247
0.048
0.024
3.347
0.016
0.012
(4, 6)
0.307
0.120
0.156
0.207
0.088
0.064
0.795
0.080
0.056
(4, 7)
6.622
0.052
0.028
10.195
0.048
0.008
16.757
0.052
0.040
(4, 8)
0.277
0.316
0.328
0.208
0.184
0.144
0.695
0.228
0.204
(5, 6)
0.482
0.164
0.212
0.511
0.132
0.160
1.067
0.204
0.220
(5, 7)
2.783
0.172
0.216
4.442
0.128
0.100
6.437
0.184
0.276
(5, 8)
0.513
0.132
0.192
0.520
0.108
0.112
1.249
0.136
0.148
(6, 7)
4.831
0.112
0.048
7.934
0.096
0.008
11.542
0.112
0.068
(6, 8)
0.115
0.900
0.972
0.053
0.656
0.680
0.239
0.772
0.760
(7, 8)
4.831
0.104
0.036
7.797
0.092
0.004
11.669
0.108
0.056As a specific example, consider the pair(
3,4
) for which the null hypothesis is accepted for each test; this formal conclusion could also be expected while looking at the standardized densities presented in Figure 1(d). It is clear from the nonstandardized densities (Figure 1(c)) that the conclusion would have been different if the data were not standardized by their respective means and variances. From the scatterplot in Figure 1(a), one can see that there is a significant positive relationship between the two random variables; it can also be seen from the inspection of the scatterplot of the normalized ranks (Figure 1(b)). It just illustrates the importance of having a statistical procedure that takes into account the possible dependence. For the pair (
4,5
), all the tests agree on a clear rejection of H
0; this conclusion is in accordance with the standardized densities that one can see in Figure 2.Lastn
=
904 observations of the pair (
3,4
) in theOil currency data set. (a) Scatterplot of the original data; (b) scatterplot of the standardized ranks; (c) estimated densities; (d) estimated standardized densities.
(a)
(b)
(c)
(d)Lastn
=
904 observations of the pair (
4,5
) in theOil currency data set. (a) estimated densities and (b) estimated standardized densities.
(a)
(b)In order to illustrate the caseK
>
2, consider the quadruplet (
1,3
,
4,8
). The four superimposed estimated densities are presented in Figure 3(a). The null hypothesis of equality of the four standardized distributions is accepted by each of the tests but by a very small amount, especially for the characteristic function statistics. Indeed, one obtains S
n
=
1.103 (PV I = 0.112, PV II = 0.172), T
n
G
1
=
0.891 (PV I = 0.108, PV II = 0.080), and T
n
G
2
=
2.734 (PV I = 0.076, PV II = 0.068). If one removes X
1 and then restricts to the triplet (
3,4
,
8
), one obtains S
n
=
0.469 (PV I = 0.404, PV II = 0.48), T
n
G
1
=
0.347 (PV I = 0.24, PV II = 0.18), and T
n
G
1
=
1.145 (PV I = 0.28, PV II = 0.216), so that the null hypothesis is clearly accepted. This conclusion concords with the standardized densities in Figure 3(b).Standardized densities of(
1,3
,
4,8
) (a) and of (
3,4
,
8
) (b) for the last n
=
904 observations in theOil currency data set.
(a)
(b)



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*Source: 523139-2014-07-13.xml*