TY - JOUR
T1 - The mean Euler characteristic and excursion probability of Gaussian random fields with stationary increments
AU - Cheng, Dan
AU - Xiao, Yimin
N1 - Funding Information:
Supported in part by NSF Grants DMS-10-06903, DMS-13-07470 and DMS-13-09856. MSC2010 subject classifications. 60G15, 60G60, 60G70
Publisher Copyright:
© Institute of Mathematical Statistics, 2016.
PY - 2016/4
Y1 - 2016/4
N2 - Let X = {X(t), t ϵ RN} be a centered Gaussian random field with stationary increments and X(0) = 0. For any compact rectangle T ⊂ RN and u ϵ R, denote by Au = {t ϵ T :X(t) ≥ u} the excursion set. Under X(·) ∈ C2(RN) and certain regularity conditions, the mean Euler characteristic of Au, denoted by E{ø(Au)}, is derived. By applying the Rice method, it is shown that, as u→∞, the excursion probability P{suptϵT X(t) ≥ u} can be approximated by E{ø(Au)} such that the error is exponentially smaller than E{ø(Au)}. This verifies the expected Euler characteristic heuristic for a large class of Gaussian random fields with stationary increments.
AB - Let X = {X(t), t ϵ RN} be a centered Gaussian random field with stationary increments and X(0) = 0. For any compact rectangle T ⊂ RN and u ϵ R, denote by Au = {t ϵ T :X(t) ≥ u} the excursion set. Under X(·) ∈ C2(RN) and certain regularity conditions, the mean Euler characteristic of Au, denoted by E{ø(Au)}, is derived. By applying the Rice method, it is shown that, as u→∞, the excursion probability P{suptϵT X(t) ≥ u} can be approximated by E{ø(Au)} such that the error is exponentially smaller than E{ø(Au)}. This verifies the expected Euler characteristic heuristic for a large class of Gaussian random fields with stationary increments.
KW - Euler characteristic
KW - Excursion probability
KW - Excursion set
KW - Gaussian random fields with stationary increments
KW - Super-exponentially small
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U2 - 10.1214/15-AAP1101
DO - 10.1214/15-AAP1101
M3 - Article
AN - SCOPUS:84964325628
SN - 1050-5164
VL - 26
SP - 722
EP - 759
JO - Annals of Applied Probability
JF - Annals of Applied Probability
IS - 2
ER -