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On (p_{1},…,p_{k})spherical distributions
Journal of Statistical Distributions and Applications volume 6, Article number: 9 (2019)
Abstract
The class of (p_{1},…,p_{k})spherical probability laws and a method of simulating random vectors following such distributions are introduced using a new stochastic vector representation. A dynamic geometric disintegration method and a corresponding geometric measure representation are used for generalizing the classical χ^{2}, t and Fdistributions. Comparing the principles of specialization and marginalization gives rise to an alternative method of dependence modeling.
Introduction
A basic notion from the theory of spherical probability laws is that of the stochastic basis which is a random vector following the uniform distribution on the Euclidean unit sphere in the kdimensional Euclidean space \(\mathbb {R}^{k}\), see e.g. Fang et al. (1990). In this monograph, multivariate uniform distributions are introduced in an algebraic way without referring to any type of surface measure. Numerous authors deal with the (singular) uniform distribution by considering the density of its k−1dimensional marginal distribution, an approach which will, however, not further be discussed, here. Instead, the point of view of uniformity of which is the speech here is to define it by having a constant RadonNikodym derivative with respect to the Euclidean surface content measure. This geometric view onto the class of spherical distributions is the background of the corresponding geometric measure representation (2) in Richter (1991). This representation extends the one given in (3) in Richter (1985) for the Gaussian law and was exploited later on in a series of papers on probabilities of large deviations and on various statistical distributions. For a related survey see e.g. Richter (2015b).
Many authors studied more general or modified multivariate distribution classes. Just for getting an impression of this research area taking into account different points of view we refer to the more recent papers Field and Genton (2006), Arnold et al. (2008), Kamiya et al. (2008), Balkema et al. (2010), Balkema and Nolde (2010), Richter (2014), Richter (2015a) and Nolan (2016) as well as the references given there.
A detailed geometric description of uniformity of the stochastic basis of l_{k,q}spherical distributions, q>0,k∈{2,3,…}, is given in Richter (2009). The mathematical background there is based upon both a study of the different notions of Euclidean and l_{k,q}surface content measures on l_{k,q}spheres and on using suitable coordinates for evaluating the uniform measure of given subsets of spheres. The coordinates mentioned were introduced in Richter (2007) solving a long standing problem apparently conclusively treated as insolvable in Szablowski (1998).
It is a natural next step of research to consider random vectors having pspherical uniform distributions with positive components of p=(p_{1},…,p_{k}). The studies of (limit laws in) high risk scenarios in Balkema and Embrechts (2007) and of natural image patches in Sinz et al. (2009), e.g., deliver well motivating examples from this direction. A stochastic matrix times vector and a dynamic geometric measure representation were proved for the corresponding twodimensional case in Richter (2017). More generally, the necessities of flexible probabilistic modeling in the era of big statistical data make it desirable to further study the class of pspherical or l_{k,p}symmetric distributions. For technical reasons, we deal exclusively with the case
here. As a consequence, distributions studied here are not invariant w.r.t. the class of orthogonal transformations or at least w.r.t. particular rotations but still appear to be signinvariant meaning invariance w.r.t. multiplication with sign matrixes. While sample schemes for identically distributed variables automatically imply exchangeability of all variables in the present paper this property is excluded due to assumption (1). Distributions considered here are therefore fully outside the scope of the spherical distribution part and in consequence of the remaining parts of the classical monograph by Fang et al. (1990) and numerous work following it.
The present paper is structured as follows. The class of pspherical uniform distributions is introduced in a geometrically motivated way in Section 2 and extended to the class of pspherical or l_{k,p}symmetric distributions in Section 3.1. The Sections 3.2 and 3.3 deal with a geometric measure representation and a combination of the principles of specialization and marginalization in dependence modeling, respectively.
The class of pspherical uniform distributions
Let us consider the functional
where the vector p=(p_{1},...,p_{k}) consists of pairwise different components throughout this paper. The set
will be called the pball with pspherical radius parameter r>0. Clearly, the ”unit ball” B_{p}=B_{p}(1) is not a norm or antinorm ball, r is not a radius in the sense of Euclidean or any l_{k,q}geometry and the Minkowski functional of B_{p}(r) is not homogeneous. But if we would allow, for a meantime ignoring assumption (1), to put p_{1}=...=p_{k}=q≥1 or p_{1}=...=p_{k}=q∈(0,1) then B_{p}(r) would be a convex norm ball or a radially concave antinorm ball with norm or antinorm radius (qr)^{1/q}, respectively. In these cases the notation B_{p}(r) would coincide with the notation B_{q}((qr)^{1/q}) in Richter (2014); Richter (2015a) where q is just a scalar while p is a kdimensional vector, here. For the notions of antinorm and radial concavity we refer to Moszyńska and Richter (2012). The functional x→x^{(p)} is invariant w.r.t. multiplication with sign matrixes, that is Sx^{(p)}=x^{(p)} if S is a diagonal matrix with entries s_{1},...,s_{k} which can be arbitrarily chosen from {−1,1}. The topological boundary S_{p}(r) of B_{p}(r) is called the psphere with pspherical radius parameter r. We call S_{p}(r) (positive) matrixhomogeneous meaning that it allows the representation S_{p}(r)=D_{p}(r)S_{p} where
is a diagonal matrix and S_{p}=S_{p}(1) denotes the ”unit sphere”. Note that B_{p}(r_{1})⊂B_{p}(r_{2}) if r_{1}<r_{2} and
Let μ and \(\mathfrak B^{k}\) denote the Lebesgue measure and the Borel σfield in \(\mathbb {R}^{k}\), respectively. Assume the random vector X follows the uniform distribution on \(\mathfrak B(B_{p})=\mathfrak B^{k}\cap B_{p}\), that is
and let R^{(p)}=X^{(p)}. What can we say then about the distribution of the random vector
The answer to this question is basic for disclosing the main message of this paper and will be given below. The sets
and
defined for \(A\in \mathfrak B^{k}\cap S_{p}=\mathfrak B(S_{p})\) are called D_{p}transformed central projection cone and D_{p}transformed ball sector, respectively. For evaluating the volume of the latter type of sets we shall use the following coordinates.
Definition 1
Let \( {\mathfrak {p}}>0\) be a parameter and \(M_{k}=(0,\infty)\times M_{k}^{*}\) where \(M_{k}^{*}=[0,\pi)^{\times (k2)}\times [0,2\pi). \) The \((p,{\mathfrak {p}})\)spherical coordinate transformation \( Pol_{p,{\mathfrak {p}},k}: M_{k}\rightarrow \mathbb {R}^{k} \) with
is defined by
Here, the qgeneralized trigonometric functions \( \sin _{q}\varphi =\frac {\sin \varphi }{N_{q}(\varphi)}\) and \( \cos _{q}\varphi =\frac {\cos \varphi }{N_{q}(\varphi)} \) with N_{q}(φ)=( sinφ^{q}+ cosφ^{q})^{1/q}, q>0 are introduced in Richter (2007) and used in studying and geometrically representing generalized spherical power exponential distributions in a series of papers starting from Richter (2009).
Remark 1
The map \(Pol_{p,{\mathfrak {p}},k}\) is almost onetoone and its inverse is given by
and
Evaluating the Jacobian of the transformation \(Pol_{p,{\mathfrak {p}},k}\), the volume of the D_{p}transformed ball sector satisfies
where
with \(J_{k}^{*}(\varphi)d\varphi \) being equal to
Here and below, the exponent of \(\sin _{\mathfrak {p}} \varphi _{k1}\) is defined to be \({\frac {\mathfrak {p}}{p_{k}}1}\) and
is the restriction of the function \(Pol_{p,{\mathfrak {p}}, k}\) to radius parameter 1.
Remark 2
If the expression in ( 7 ) is equivalently rewritten by substituting x_{i} = cosφ_{i}, i = 1,...,k then \(\pi _{p}^{*}(A)\) can be represented as
where
and
The following definition is well motivated by the fact that for the case p=(q,…,q) (not considered here) the l_{k,q}surface content measure is similarly introduced in Richter (2009) and proved to be equivalent to the differentialgeometric definition. Much more general results of this type of equivalence are proved for norm and antinorm spheres in Richter (2015a).
Definition 2
Let \(f_{A}(r)=\mu (Se_{p}(A,r))\text { for } r>0\text { and } A\in \mathfrak {B}(S_{p})\). We call
the pspherical surface content of D_{p}(r)A or its S_{p}surface content, for short.
It follows from this definition and equation (6) that
Let us emphasize again that differently from what is assumed in a broad literature p is a vector, here. In particular, as because
and
there holds
Here,
denotes the poly Beta function where \(\Gamma (x)=\int \limits _{0}^{\infty }t^{x1}e^{t}dt, x>0\) is the Gamma function, and B(.,.) is the classical Beta function. Moreover,
Definition 3
The density
is called angular Beta density with parameters \({\mathfrak {p}}>0, a>0\) and b>0.
Remark 3
The notion of S_{p}surface content of D_{p}(r)A is different from the notion of Euclidean surface content of D_{p}(r)A (unless for the case p=(2,...,2) which, however, because of assumption ( 1 ) is not allowed to appear, here).
Definition 4
The pspherical uniform probability law on the Borel σfield \(\mathfrak B(S_{p})\) is defined by
This definition corresponds to Definition 8 in Richter (2007) where, however, p is a scalar while it is a kdimensional vector, here. Similarly, the notation \(U_{p}=D_{p}\left (\frac {1}{R^{(p)}}\right)X\) introduced above is closely connected to that given in the same paper where p is a scalar. However, while \(R^{(p)}=\frac {X_{1}^{p_{1}}}{p_{1}}+...+\frac {X_{k}^{p_{k}}}{p_{k}}\) denotes a certain ”mixedpowerofradius”, here, not being the power of a norm or antinorm, in Richter (2007) the symbol R actually means a norm or antinorm. The proof of the following theorem is analogous to that of Theorem 2 in Richter (2017) and will therefore be omitted, here.
Theorem 1

(a) The random vector U_{p} follows the pspherical uniform distribution, U_{p}∼ω_{p}, is independent of R^{(p)}, and R^{(p)} has the following density with respect to the Lebesgue measure on the real line
$$ \left(\frac{1}{p_{1}}+...+\frac{1}{p_{k}}\right)r^{\frac{1}{p_{1}}+...+\frac{1}{p_{k}}1}I_{[0,1)}(r) dr. $$(12) 
(b) If, vice versa, ξ and W are independent where ξ has density ( 12 ) and W∼ω_{p} then η=D_{p}(ξ)·W is uniformly distributed on the unit ball B_{p}.
We are now in a position to disclose the basic message of this paper as follows: matrixmultiplication of a pspherical uniformly distributed random vector U_{p} by D_{p}(R) where U_{p} and the random variable R≥0 are independent generates the world of distributions being of main interest, here.
Remark 4
If the random variable Y is uniformly distributed on (0,1) then \(Z_{p}=Y^{1/\left (\frac {1}{p_{1}}+...+\frac {1}{p_{k}}\right)}\) follows the density ( 12 ), that is \(Z_{p}\overset {d}{=}{R^{(p)}}.\) Thus R^{(p)} can be simulated by Z_{p}.
Theorem 2
Let the random vector X be uniformly distributed on the unit pball B_{p}. Then the pspherical radius variable R^{(p)} and the pspherical angles Φ_{1},...,Φ _{k−1} of X are independent and the angle Φ_{i} follows the angular Beta density with parameters \({\mathfrak {p}}, a_{i}\) and \(b_{i}, f_{{\mathfrak {p}},a_{i},b_{i}}\), where \(a_{i}=\frac {\mathfrak {p}}{p_{i}}, b_{i}=\frac {\mathfrak {p}}{p_{k}}+...+\frac {\mathfrak {p}}{p_{i+1}}, i=1,...,k2\) and \(a_{k1}=\frac {\mathfrak {p}}{p_{k1}}, b_{k1}=\frac {\mathfrak {p}}{p_{k}} \).
Proof
According to Definition 1, the vector U_{p} allows the representation
where the signature vector S=(S_{1},…,S_{k})^{T} is independent of Φ_{1},…,Φ_{k−1} and uniformly distributed in {−1,1}^{×k}. It follows from the evaluation of the Jacobian of the transformation \(Pol_{p,{\mathfrak {p}},k}\) that the vector (R^{(p)},Φ _{1},...,Φ _{k−1})^{T} has the density
where \(J_{k}^{*}\) satisfies representation (7). For i=1,...,k−1, the independent angles Φ_{i} thus have the densities
Now, Definition 3 applies. □
Remark 5
If Φ_{i} follows the density in ( 14 ) then \(Y=\cos _{\mathfrak {p}}\Phi _{i}^{\mathfrak {p}}\sim B(l,m)\) where \( l=\frac {1}{p_{i}}, m=\frac {1}{p_{i+1}}+...+\frac {1}{p_{k}}.\) Let (V_{0,1},V_{0,2}) be uniformly distributed in (0,1)×(0,1), then there holds
This allows to simulate Y by an acceptancerejection method, see equation (A5) and algorithm A.1, step 2 in Kalke and Richter (2013). Thus, methods for simulating vectors U_{p} and X being pspherical uniformly distributed on S_{p} and uniformly distributed on B_{p},U_{p}∼U(S_{p}) and X∼U(B_{p}), respectively, can now be established as follows.
Simulation Algorithm 1[pspherical uniform distribution inS_{p},p=(p_{1},...,p_{k})^{T}] Step 1 For i∈{1,…,k1}, simulate (V_{i,1},V_{i,2})∼U([0,1]×[0,1])until \(V_{i,1}^{p_{i}}+V_{i,2}^{1/\left (\frac {1}{p_{i+1}}+\ldots +\frac {1}{p_{k}}\right)}\leq 1\). Step 2 Calculate \(W_{i}=\frac {V_{i,1}^{p_{i}}}{V_{i,1}^{p_{i}}+V_{i,2}^{1/\left (\frac {1}{p_{i+1}}+\ldots +\frac {1}{p_{k}}\right)}}, i=1,...,k1\). Step 3 Simulate independently (S_{1},…,S_{k})∼U({−1,+1}^{×k}). Step 4 Calculate \(U_{p,i}=S_{i}\left (\prod \limits _{j=1}^{i1}(1W_{j})W_{i}\right)^{1/p_{i}}\) for i=1,…,k−1
and \(U_{p,k}=S_{k}\prod \limits _{j=1}^{k}(1W_{j})^{1/p_{k}}\). Step 5 Return U_{p}=(U_{p,1},...,U_{p,k})^{T}.
Simulation Algorithm 2 [Uniform distribution inB_{p},p=(p_{1},...,p_{k})^{T}] Step 1 Simulate U_{p}∼U(S_{p}) according to Algorithm 1. Step 2 Simulate independently: Y∼U(0,1).
Calculate \({R^{(p)}}=Y^{1/(\frac {1}{p_{1}}+\ldots +\frac {1}{p_{k}})}\). Step 3 Return X=R^{(p)}·U_{p}
Remark 6
By symmetry, the distribution center of U_{p} is \(\mathbb {E} (U_{p})=0_{k}\) where \(0_{k}=(0,...,0)^{T}\in \mathbb {R}^{k}\) is the zero vector of the sample space. The vector U_{p} has uncorrelated components. Using formulas (9), (13) and (14) one can show that the variances of Up′s components are
where γ_{1}=...=γ_{k−2}=2 and γ_{k−1}=γ_{k}=4.
Remark 7

(a) Let us call
$$sm_{p}(A)=\frac{\mu(Se_{p}(A,1))}{\mu(B_{p})} $$the D_{p}transformedsector measure on \(\mathfrak B(S_{p})\) (or, more precisely, the uniform probability measure of the D_{p}transformed sector Se(A,1) of B_{p}). It follows from the obvious equations
$$\frac{O_{P}(A)}{O_{P}(S_{p})}=\frac{f_{A}'(1)}{f_{S_{p}}'(1)} =\frac{\pi_{p}^{*}(A)}{\pi_{p}^{*}(S_{p})}=\frac{\mu(Se_{p}(A,1))}{\mu(Se_{p}(S_{p},1))} =\frac{\mu(Se_{p}(A,1))}{\mu(B_{p})} $$that
$$\omega_{p}(A)=sm_{p}(A). $$Thus, the pspherical uniform probability law ω_{p} can also be called a D_{p}transformedsector measure. For an interpretation of ω_{p} as cone measure see (e) and Remark 9 (b).

(b) According to Remark 3, the notion of pspherical uniform distribution on \(\mathfrak B(S_{p})\) is different from the notion of uniform distribution with respect to Euclidean surface content (unless for p_{1}=...=p_{k}∈{1,2,∞}).

(c) Fine properties of the Euclidean surface content measure defined on the Borel σfield of the Euclidean unit sphere, a precursor of the S_{p}surface content measure O_{p} considered here, are exploited by the author in the eighties and nineties of the last century in a series of papers on large deviations. A main idea in the background of those work is the development and application of a generalized method of indivisibles extending a classical approach by Cavalieri and Torricelli, see Richter (1985), Richter (2015b) and Günzel et al. (2012).

(d) It is a challenging problem to find a differentialgeometric interpretation of O_{p} as it was found in Richter (2009) for the l_{k,q}surface content measure and in Richter (2015a) for norm and antinorm spheres. This problem was first stated for the twodimensional case in Richter (2017).

(e) Several authors who study uniform distributions on (non D_{p}transformed) generalized spheres make use of the notion cone measure instead of sector measure and rely on the last representation in (a), that is on relating volumes to each other, see e.g. Naor and Romik (2003) and Barte et al. (2005) and a series of follow up papers.

(f) Only a few days before finishing the present paper, Amir AhmadiJavid kindly let me know his joint article AhmadiJavid and Moeini (2019) where the authors follow another way of considering a uniform distribution on \(\mathfrak B(S_{p})\). They start, in Definition 2.3, with a random vector being uniformly distributed in a parallelepiped and, referring to the work of Schechtman and Zinn (1990), Rachev and Rüschendorf (1991), Song and Gupta (1997), Liang and Ng (2008), Harman and Lacko (2010) and Lacko and Harman (2012), later make use of a common (nondynamical) notion of cone measure (just like the one mentioned in (e)) for studying a certain type of uniform distributions on \(\mathfrak B(S_{p})\). A closer comparison with the method presented here, where we start with a uniform distribution on a ball B_{p} and continue with a dynamically transformedcone measure, would be of interest for future work.
The class of pspherical distributions
A random vector distributed according to the pspherical uniform distribution builds the stochastic basis of any pspherical distributed random vector considered in Section 3.1. Examples of light and heavy distribution centers and tails are possible. A geometric measure representation and its applications allow studying exact distributions of generalized χ^{2}, t and Fstatistics in Section 3.2. The final Section 3.3 gives a sketch of an alternative approach to describing dependence of random variables following onedimensional specializations of kdimensional distributions instead of marginal distributions.
3.1 Definitions and Examples
Definition 5
Let the random vector U_{p} follow the pspherical uniform distribution on the Borel σfield \(\mathfrak B(S_{p}), U_{p}\sim \omega _{p},\) and R be a nonnegative random variable having cumulative distribution function (cdf) F and characteristic function (cf) ϕand being independent of U_{p}, then
is said to follow the pspherical distribution \(\Phi _{p}^{cdf(F)}=\Phi _{p}^{cf(\phi)}\). The vector U_{p} is called the pspherical uniform basis and R the generating variate of X, and (15) a stochastic representation of X. The distribution of X will alternatively be denoted \(\Phi _{p}^{pdf(f)}\) if R has probability density function (pdf) f.
Remark 8
If \(\mathbb {E} \left (R^{(p)\frac {1}{\min \{p_{1},...,p_{k}\}}}\right)\) is finite then, due to Remark 6, \(\mathbb {E}(X)=0_{k}\) and if \(\mathbb {E} \left (R^{(p)\frac {2}{\min \{p_{1},...,p_{k}\}}}\right)<\infty \) then \(\mathbb V(X_{i})=\mathbb {E}\left (R^{(p)\frac {2}{p_{i}}}\right)\mathbb V (U_{p,i}), i=1,...,k.\) For the derivation of moments in the case p_{1}=...=p_{k} we refer to ArellanoValle and Richter (2012).
Theorem 3
The characteristic function of a pspherically distributed random vector X satisfying representation ( 15 ) can be written a
where P^{R} and \(\phi _{U_{p}}\) denote the distribution law of R and the characteristic function of U_{p},respectively.
Proof
Because of the diagonal structure of D_{p}(r) we have
If \(\mathbb {E}(YR)\) denotes the conditional expectation of Y given R=r then
from where the result follows by independence of U_{p} and R. □
Corollary 1

(a) The distribution of a pspherically distributed random vector X is uniquely determined by the distribution of its generating variate R.

(b) If a pspherically distributed random vector X has a density, then it is of the form f_{X}=φ_{g;p},
$$ \varphi_{g;p}(x)= C(g;p)g\left(x^{(p)}\right), x\in\mathbb{R}^{n} $$where g:[0,∞)→[0,∞) is a density generating function (dgf) satisfying
$$ 0<I(g;p)=\int\limits_{0}^{\infty} r^{\frac{1}{p_{1}}+...+\frac{1}{p_{k}}1}g(r)dr<\infty, $$and the normalizing constant allows the factorization
$$ \frac{1}{C(g;p)}=I(g;p)O_{p}(S_{p}). $$This density is invariant w.r.t. multiplication with sign matrices, or signinvariant or signsymmetric. For a general class of symmetric distributions we refer to ArellanoValle et al. (2002) and ArellanoValle and del Pino (2004).
Both this result and the following definition transfer earlier statements from Richter (2014) to the present case. The following definition adopts notation in Müller and Richter (2016) and is aimed to make the notion of dgf unique.
Definition 6
A dgf g satisfying the equation
is called density generator (dg) of a continuous pspherical distribution.
Example 1
(a) The dg of the pspherical Kotz type distribution having parameters \(M>1\frac {1}{p_{1}}...\frac {1}{p_{k}}\) and β and γ from (0,∞) is
where
The pspherical Kotz type pdf with parameters M,β,γ is therefore
(b) Particularly, the dg of the pspherical power exponential distribution is
and one may write then
to denote the kdimensional ppower exponential density where \(C_{i}=\frac {p_{i}^{11/p_{i}}}{2\Gamma (1/p_{i}))}, i=1,...,k \) are individual normalizing constants. In this case the random vector X consists of independent components, and according to (a) there holds
Example 2
The dg of the pspherical Pearson Type VII distribution having parameters \(M>\max \left \{1,\frac {1}{p_{1}}+...+\frac {1}{p_{k}}\right \}\) and ν>0 is
where
The pspherical Pearson Type VII density with parameters M and ν is therefore
3.2 Geometric measure representation
The following theorem is a geometricmeasure theoretic counterpart to Theorem 3. Its proof follows the line of author’s earlier work in Richter(1985, 1991, 2014, 2017). The main aim of this section is to present first applications of the geometric measure representation extending the classical HelmertPearson χ^{2}, Gosset alias Student t and Fisher Fdistributions.
Let \(\Phi _{g}^{(p)}\) denote the continuous pspherical distribution law having dg g.
Theorem 4
For every \(B\in \mathfrak B^{k},\)
Let a random vector X follow the pspherical distribution law with dg g, X∼Φ_{g;p}, and T=T(X) be any statistic. The statistic T satisfies T<λ if and only if the outcome of X belongs to the sublevel set
thus
Example 3
Chip statistic Let
denote the pspherical radius variable of the random vector X, that is T(X)=R^{(p)}, then the ipf of the set B_{T} satisfies
The Chi (g;p) pdf is therefore
Differently from the χ^{2}(k)distribution where the parameter k corresponds to the dimension of a subspace of the sample space, here, the parameter \(\frac {1}{p_{1}}+...+\frac {1}{p_{k}}\) itself does not allow interpretation as dimension of a linear space or a linear subspace of the sample space, but its number of summands k does.
Note that the density of T was dealt with for the particular dg of the generalized Gaussian law in Taguchi (1978). For \(\frac {1}{p_{1}}+...+\frac {1}{p_{k}}=\frac {k}{2}\) the pdf in ( 20 ) is called the ggeneralization of the Chisquare density with k degrees of freedom (d.f.) in Richter (1991) (note that there holds p_{1}=…=p_{k} but ( 16 ) is not assumed to be satisfied there). For more partial cases and statistical applications of this distribution, see Richter (2007, 2009, 2016).
If we specify \(g=g_{PE}^{(p)}\) in ( 19 ), see Example 1(b), then
In this case we have
and
as well as
It follows from Remarks 6 and 8 that
Similarly, if we put \(g=g^{(p)}_{Kt;M,\beta,\gamma }\) or \(g=g^{(p)}_{PT7;M,\nu }\) in ( 19 ) then \(\phantom {\dot {i}\!}f_{R^{(p)}}\) and \(\mathbb V(X_{i})\) will be correspondingly specified.
Example 4
Fisherp statistic Let the vectors X^{(1)T}=(X_{1},...,X_{m}) and X^{(2)T}=(X_{m+1},...,X_{k})be subvectors of X=(X_{1},...,X_{m},X_{m+1},...,X_{k})^{T} and p^{(1)}=(p_{1},...,p_{m})^{T},p^{(2)}=(p_{m+1},...,p_{k})^{T} be subvectors of the shapetail parameter vector p=(p_{1},...,p_{m},p_{m+1},...,p_{k})^{T}, and assume that X∼Φ_{g;p}. We consider the Fp statistic
and recognize that T(x)=T(D_{p}(γ)x) for all γ>0. Roughly spoken, B_{T} has the curved conetype property
Thus the ipf of the set B_{T} does not depend on r and equation (19) shows that
Making use of the coordinate transformations
and
instead of the coordinate transformations SPH_{p,1} and SPH_{p,2} used in Richter (2009), and
and further following the line of the proof of Theorem 6 there, we get
Taking the derivative shows that the pdf of statistic T is
We call the pdf in ( 23 ) the Fisherp density with (m,k−m) d.f. or F_{m,k−m}(p)density, for short.
Example 5
Studentp statistic Let the tp statistic be defined by
The pdf of T is called Student pdensity with k−1 d.f. or \(t_{k1}\left (\frac {1}{p_{1}},...,\frac {1}{p_{k}}\right)\)density, for short:
Note that, as in Example 4, B_{T} is a D_{p}transformedcone or curvedtransformedcone type set satisfying ( 22 ).
Remark 9

(a) Because Fisherp and Studentp distributions do not depend on the dg, the Fp and tp statistics are called grobust. For a study of gsensitivity and grobustness of certain statistics see Ittrich et al. (2000) and for a study of statistics generating curvedtransformedcone type sets, see Ittrich and Richter (2005).

(b) Let B_{T}(λ) be a curvedtransformedcone type set satisfying ( 22 ) and put A=B_{T}(λ)∩S_{p}. By Theorem 4, \(\Phi ^{(p)}_{g}(B_{T}(\lambda))=\omega _{p}(A).\) It is reasonable therefore to call C^{(p)}(A)=ω_{p}(A) the D_{p}transformedcone measure of \(A\in \mathfrak B(S_{p})\).

(c) We recall that several representations of Student distributed statistics were given in Richter (1995). In particular, the two facts are exploited there that the ipf of the cone {T<λ} does not depend on its radius variable and that the multivariate standard Gaussian law is invariant w.r.t. orthogonal transformations, together leading to grobustness of Fisher’s and Student’s statistics. Due to assumption ( 22 ), we observe in the present situation that the ipf of B_{T}(t) does not depend on the generalized radius variable and we observe invariance of Fisher’s and Student’s statistics w.r.t. any transformation D_{p}(r) where r>0 is used to prove grobustness of T.Thus, if an arbitrary statistic T generates sublevel sets satisfying assumption ( 22 ) then such statistic is grobust.
3.3 Dependence modeling: specialization vs. marginalization
Let 1≤m<k,1≤i_{1}<i_{2}<…<i_{m}≤k. We assume that
and
are suitably normalized densities and call them mdimensional specializations of the Kotz and Pearson type densities \(\varphi ^{(p)}_{Kt;M,\beta,\gamma }\) and \(\varphi ^{(p)}_{PT7;M,\nu }\), respectively. It is well known that marginal densities are not of the same type as specializations, in general. For the well known possibilities and problems of finding marginal densities of elliptically contoured distributions we refer to Fang et al. (1990).
Imagine now each of k experimenters observe another random variable, let them combine their (possibly dependent) observations by a vector and describe this vector by a joint cdf F^{(k)} (possibly including dependence). In hindsight, did the experimenters construct the multivariate cdf F^{(k)} starting from the specializations F_{1},...,F_{k} of F^{(k)} or from the marginal cdfs \(F_{1}^{*},...,F_{k}^{*}\) of F^{(k)}? In other words, are the experimenters searching for a multivariate cdf F^{(k)} such that their original observations follow marginal distributions or specializations of F^{(k)}? For an illustration, in the case p_{1}=...=p_{k} and at hand of certain stock exchange indices, see Müller and Richter (2016).
A common way of studying dependence among components of random vectors makes use of marginal distributions and copulas. Here, we approach dependence by comparing a vector density with the product of all its onedimensional specializations. To this end, let
where \(P(x)=\prod \limits _{i=1}^{k}f_{i}(x_{i})\) and J is the joint density which combines f_{1},…,f_{k} by a certain dependence construction.
For comparison, let x→c^{(p)}(x),F_{i} and g_{i},i=1,...,k denote the Copula density, the marginal cdfs and pdfs of the distribution law \(\Phi ^{(p)}_{g}\), respectively. Then
The following definition is therefore well motivated.
Definition 7
We call c_{sp} the specialization copula density.
Example 6
Each of the functions
is a onedimensional specialization of the Kotz type density ( 17 ) meaning that, vice versa, φ_{Kt;M,β,γ} in ( 17 ) generalizes f_{i},i=1,…,k. In other words, the function in ( 17 ) is thought being build by a certain dependence construction applied to f_{1},...,f_{k}. Thus,
and \(\varphi ^{(p)}_{Kt; M,\beta,\gamma }\) allows the representation
where the specialization copula density is explicitly given by
with
Clearly, searching for marginal densities is not as easy as determining specializations, here, and the corresponding copula density has not such nice structure.
Example 7
Each of the functions
is a onedimensional specialization of the Pearson Type VII pdf in ( 18 ). The pdf \(\varphi _{PT7;M,\nu }^{(p)}\) allows the representation
where the dependence function is
with
As in the preceding example, the specialization copula density has a nice structure and is explicitly given.
Remark 10
The univariate qgeneralized normal distribution or qpower exponential distribution has been parameterized in different ways in the literature, for a recent survey see Dytso et al. (2018). For different purposes, any of these parameterizations can be used to derive modified representations of the distributions considered in this paper.
Model extensions: a concluding remark
Although Definition 5 deals with the whole class of pspherical distributions later consideration is concentrated on continuous pspherical distributions. To finally widen again the view we refer to Remark 1 in Richter (2015a) where a way is described to derive new distributions from the elements of a given class of distributions by restricting the region of definition of such distributions. The following definition sums up that for the present situation.
Definition 8
Let \(\Sigma \in \mathfrak B(S_{p})\) with O_{p}(Σ)>0 and
Then ω_{Σ} is called puniform distribution on \(\mathfrak B(\Sigma)\).
The extension of the whole class of pspherical distributions follows accordingly.
It might be a further task of future work to extend the class of qspherical processes, q>0, introduced in Müller and Richter (2019) to a class of pspherical processes, p=(p_{1},...,p_{k})∈(0,∞)^{×k}.
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Richter, W. On (p_{1},…,p_{k})spherical distributions. J Stat Distrib App 6, 9 (2019). https://doi.org/10.1186/s404880190097z
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Keywords
 (p _{1},...,p _{k})power exponential distribution
 simulation of (p _{1},...,p _{k})spherical uniform distribution
 signinvariant distribution
 (p _{1},...,p _{k})spherical surface content measure
 (p _{1},...,p _{k})spherical radius variable
 matrix times vector stochastic representation
 dynamic disintegration
 (p _{1},...,p _{k})spherical coordinates
 generalized χ ^{2}
 t and Fdistributions
 dependence modeling
 specialization vs. marginalization
 specialization Copula density
 poly Beta function
 angular Beta density
Mathematics Subject Classification (2000)
 60E05
 62E15
 60D05
 14J29
 28A50
 28A75