Abstract

Let Wi={Wi(t),t∈R+},i=1,2W_i=\{W_i(t), t\in \mathbb{R}_+\}, i=1,2 be two Wiener processes and W3={W3(t),t∈R+2}W_3=\{W_3(\mathbf{t}), \mathbf{t}\in \mathbb{R}_+^2\} be a two-parameter Brownian sheet, all three processes being mutually independent. We derive upper and lower bounds for the boundary non-crossing probability Pf=P{W1(t1)+W2(t2)+W3(t)+h(t)≤u(t),t∈R+2},P_f=P\{W_1(t_1)+W_2(t_2)+W_3(\mathbf{t})+h(\mathbf{t})\leq u(\mathbf{t}), \mathbf{t}\in\mathbb{R}_+^2\}, where h,u:R+2→R+h, u: \mathbb{R}_+^2\rightarrow \mathbb{R}_+ are two measurable functions. We show further that for large trend functions γf>0γf>0 asymptotically when γ→∞γ\to \infty we have that ln⁡Pγf\ln P_{γf} is the same as ln⁡Pγf‾\ln P_{γ\underline{f}} where f‾\underline{f} is the projection of ff on some closed convex set of the reproducing kernel Hilbert Space of WW. It turns out that our approach is applicable also for the additive Brownian pillow.

Explore this paper’s authors, topics and related work.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Reviews 0

Write a reviewWrite

No reviews yet.

Discussion 0

Add a commentWrite

No comments yet.