i
√n
i
Σn
[0,1]
Pn(di)μ∗(dy) for t ∈ R
it
exp
Φn(t) =
ϕ(f 1(y)) + ·· · + ϕ(fn(y))
and
Φn(t) =
x
ϕ(f 1(x)) + ·· · + ϕ(fn(x))
Φn(t) → exp
−t2σ2
as n → ∞.
2
Therefore, to finish the proof it is sufficient to show for every t ∈ R the conver- gence
x
|Φn(t) − Φn(t)|→ 0 as n → ∞.
Since |eitx1 − eitx2 |≤ |t||x1 − x2| for every t ∈ R, for sufficiently large n ∈ N
and x ∈ [εn, 1 − εn] using μ∗ ∈ PM,α we have
| |x
t
|Φ (t) − Φn(t)| ≤√ ·
(ϕ(fj(x)) − ϕ(fj(y))) Pn(di)μ∗(dy)
n n
|t|
[0,1]
i i
Σn j=1
Σ
i
i
n
n
[ε ,1−ε ]
≤√
(ϕ(fj(x)) − ϕ(fj(y))) Pn(di)μ∗(dy)
n
|t|
n n j=1
α
+ √n · 2n∗ϕ∗Mεn
n
|t|
n
[ε ,1−ε ]
A \D
i
i
≤√
(ϕ(fj (x))−ϕ(f j(y))) Pn(di)μ∗(dy)
n
|t|
n n n
α
j=1
|t|
+ √n · 2n∗ϕ∗Mεn + √n · 2n∗ϕ∗(Pn(Σn \ An)+ Pn(Dn)).
Since εn → 0 as n → ∞, for every x ∈ (0, 1) and t ∈ R, by (5.2) and using the
estimates Pn(Bn) ≤ (1 − β) √n⊗ and P (D ) ≤ 2Mγ , we finally obtain
8
n
n n
lim
n→∞
|Φ n( t) − Φ n
|≤
lim
n→∞
2|t|
n
√
x
n
3
(C1Ln 8 + n
∗ ϕ∗(1 − β) √8 n⊗)
This completes the proof.
+ lim
n→∞
2|t|∗ϕ∗√n((1 − δ) √4 n⊗ + Mεα) = 0.
Corollary 2: If the assumptions of Theorem 4 are satisfied, then the sta-
n
k=0
tionary sequence ( √1 [nt] ϕ(Xk)) converges in distribution to σW (t), where
W (t), 0 ≤ t ≤ 1 is a Brownian motion. Moreover, for μ∗-a.e. x ∈ (0, 1) the
n
k=0
k
sequence ( √1 [nt] ϕ(Xx)) also converges in distribution to σW (t), 0 ≤ t ≤ 1.
— n
∞
Proof. From the proof of Theorem 4 it follows that the Maxwell–Woodroofe condition
n =1
n
3
j=1
Ujϕ < ∞
L2(μ∗ )
is satisfied. Applying Theorem 1.1 in [28] we obtain the first assertion. On the other hand, the second assertion follows from Theorem 2.7 in [9].
Acknowledgments. The authors wish to express their gratitude to an anony- mous referee for thorough reading of the manuscript and valuable remarks.
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