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A limit theorem for the last exit time over a moving nonlinear boundary for a Gaussian process. / Karagodin, Nikita .

в: Probability and Mathematical Statistics, Том 42, № 2, 2022, стр. 195-217.

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Karagodin, Nikita . / A limit theorem for the last exit time over a moving nonlinear boundary for a Gaussian process. в: Probability and Mathematical Statistics. 2022 ; Том 42, № 2. стр. 195-217.

BibTeX

@article{8da45093f3504b859f3d8e998ddb4d1d,
title = "A limit theorem for the last exit time over a moving nonlinear boundary for a Gaussian process",
abstract = "We prove a limit theorem on the convergence of the distributions of the scaled last exit time over a slowly moving nonlinear boundary for a class of Gaussian stationary processes. The limit is a double exponential (Gumbel) distribution.",
keywords = "last exit time, nonlinear boundary, Gaussian process, limit theorem, double exponential law",
author = "Nikita Karagodin",
year = "2022",
doi = "10.48550/arXiv.2110.01046",
language = "English",
volume = "42",
pages = "195--217",
journal = "Probability and Mathematical Statistics",
issn = "0208-4147",
publisher = "PWN",
number = "2",

}

RIS

TY - JOUR

T1 - A limit theorem for the last exit time over a moving nonlinear boundary for a Gaussian process

AU - Karagodin, Nikita

PY - 2022

Y1 - 2022

N2 - We prove a limit theorem on the convergence of the distributions of the scaled last exit time over a slowly moving nonlinear boundary for a class of Gaussian stationary processes. The limit is a double exponential (Gumbel) distribution.

AB - We prove a limit theorem on the convergence of the distributions of the scaled last exit time over a slowly moving nonlinear boundary for a class of Gaussian stationary processes. The limit is a double exponential (Gumbel) distribution.

KW - last exit time

KW - nonlinear boundary

KW - Gaussian process

KW - limit theorem

KW - double exponential law

U2 - 10.48550/arXiv.2110.01046

DO - 10.48550/arXiv.2110.01046

M3 - Article

VL - 42

SP - 195

EP - 217

JO - Probability and Mathematical Statistics

JF - Probability and Mathematical Statistics

SN - 0208-4147

IS - 2

ER -

ID: 98431138