Optimality
Optimality
Optimality
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196 V. H. de la Peña, R. Ibragimov and S. Sharakhmetov<br />
to the study of convergence of the measures of intertemporal dependence of the time<br />
series, including the above multivariate Pearson coefficient φ, the relative entropy δ,<br />
the divergence measures D ψ and the mean information for discrimination between<br />
the dependence and independence I(f0, f; Φ). We obtain the following Theorem 7.2<br />
which deals with the convergence in distribution of m-dimensional statistics of time<br />
series.<br />
Let h : R m → R be an arbitrary function of m arguments, Y be some r.v. and<br />
let ψ be a convex function increasing on [1,∞) and decreasing on (−∞, 1) with<br />
ψ(1) = 0. In what follows, D → represents convergence in distribution. In addition,<br />
{ξn i } and{ξt} stand for dependent copies of{X n i } and{Xt}.<br />
Theorem 7.2. For the double array {Xn i }, i = 1, . . . , n, n = 0,1, . . . let func-<br />
tionals φ2 n,n = φ2 Xn 1 ,Xn 2 ,...,Xn n , δn,n = δXn 1 ,Xn 2 ,...,Xn n , Dψ n,n = D ψ<br />
Xn 1 ,Xn 2 ,...,Xn, ρ(q) n,n =<br />
n<br />
ρ (q)<br />
X n 1 ,Xn 2 ,...,Xn n , q∈ (0, 1),Hn,n = (1/2ρ (q)<br />
n,n) 1/2 , n = 0, 1,2, . . . denote the corresponding<br />
distances. Then, as n→∞, if<br />
n�<br />
i=1<br />
ξ n i<br />
D<br />
→ Y<br />
and either φ 2 n,n→ 0, δn,n→ 0, D ψ n,n→ 0, ρ (q)<br />
n,n→ 0 orHn,n→ 0 as n→∞, then<br />
as n→∞,<br />
n�<br />
i=1<br />
X n i<br />
D<br />
→ Y.<br />
For a time series{Xt} ∞ t=0 let the functionals φ 2 t = φ 2 Xt,Xt+1,...,Xt+m−1 , δt =<br />
δXt,Xt+1,...,Xt+m−1, D ψ<br />
t = D ψ<br />
, ρ(q)<br />
Xt,Xt+1,...,Xt+m−1 t = ρ (q)<br />
, q∈ (0, 1),<br />
Xt,Xt+1,...,Xt+m−1<br />
Ht = (1/2ρ (q)<br />
t ) 1/2 , t = 0, 1,2, . . . denote the m-variate Pearson coefficient, the<br />
relative entropy, the multivariate divergence measure associated with the function<br />
ψ, the generalized Tsallis entropy and the Hellinger distance for the time series,<br />
respectively.<br />
Then, if, as t→∞,<br />
h(ξt, ξt+1, . . . , ξt+m−1) D → Y<br />
and either φ 2 t → 0, δt→ 0, D ψ<br />
t → 0, ρ (q)<br />
t → 0 orHt→ 0 as t→∞, then, as<br />
t→∞,<br />
h(Xt, Xt+1, . . . , Xt+m−1) D → Y.<br />
From the discussion in the beginning of the present section it follows that in the<br />
case of Gaussian processes{Xt} ∞ t=0 with (Xt, Xt+1, . . . , Xt+m−1)∼N(µt,m,Σt,m),<br />
the conditions of Theorem 7.2 are satisfied if, for example,|Rt,m(2Im− Rt,m)|→1<br />
or|Σt,m|/ �m−1 i=0 σ2 t+i → 1, as t → ∞, where Rt,m denote correlation matrices<br />
corresponding to Σt,m and (σ2 t , . . . , σ2 t+m−1) = diag(Σt,m). In the case of processes<br />
{Xt} ∞ t=1 with distributions of r.v.’s X1, . . . , Xn, n≥1, having generalized Eyraud–<br />
Farlie–Gumbel–Morgenstern copulas (3.3) (according to [70], this is the case for any<br />
time series of r.v.’s assuming two values), the conditions of the theorem are satisfied<br />
if, for example, φ2 t = �m �<br />
c=2 i1