CONVERGENCE OF THE CENTERED MAXIMUM OF LOG-CORRELATED GAUSSIAN FIELDS
We show that the centered maximum of a sequence of logarithmically correlated Gaussian fields in any dimension converges in distribution, under the assumption that the covariances of the fields converge in a suitable sense. We identify the limit as a randomly shifted Gumbel distribution, and charact...
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| Veröffentlicht in: | The Annals of probability 2017-11, Vol.45 (6A), p.3886-3928 |
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| container_issue | 6A |
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| container_title | The Annals of probability |
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| creator | Ding, Jian Roy, Rishideep Zeitouni, Ofer |
| description | We show that the centered maximum of a sequence of logarithmically correlated Gaussian fields in any dimension converges in distribution, under the assumption that the covariances of the fields converge in a suitable sense. We identify the limit as a randomly shifted Gumbel distribution, and characterize the random shift as the limit in distribution of a sequence of random variables, reminiscent of the derivative martingale in the theory of branching random walk and Gaussian chaos. We also discuss applications of the main convergence theorem and discuss examples that show that for logarithmically correlated fields; some additional structural assumptions of the type we make are needed for convergence of the centered maximum. |
| doi_str_mv | 10.1214/16-AOP1152 |
| format | Article |
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| ispartof | The Annals of probability, 2017-11, Vol.45 (6A), p.3886-3928 |
| issn | 0091-1798 2168-894X |
| language | eng |
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| source | JSTOR Mathematics and Statistics; Project Euclid; JSTOR; EZB Electronic Journals Library |
| subjects | Chaos theory Convergence Correlation Correlation analysis Martingales Normal distribution Random variables Random walk Random walk theory Studies |
| title | CONVERGENCE OF THE CENTERED MAXIMUM OF LOG-CORRELATED GAUSSIAN FIELDS |
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