TY - JOUR

T1 - On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions

AU - Wadsworth, Jennifer

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version Jennifer L. Wadsworth On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions Biometrika (2015) 102 (3): 705-711 first published online June 25, 2015 doi:10.1093/biomet/asv029 is available online at: http://biomet.oxfordjournals.org/content/102/3/705

PY - 2015/9

Y1 - 2015/9

N2 - Full likelihood-based inference for high-dimensional multivariate extreme value distributions, or max-stable processes, is feasible when incorporating occurrence times of the maxima; without this information, ddd-dimensional likelihood inference is usually precluded due to the large number of terms in the likelihood. However, some studies have noted bias when performing high-dimensional inference that incorporates such event information, particularly when dependence is weak. We elucidate this phenomenon, showing that for unbiased inference in moderate dimensions, dimension ddd should be of a magnitude smaller than the square root of the number of vectors over which one takes the componentwise maximum. A bias reduction technique is suggested and illustrated on the extreme-value logistic model.

AB - Full likelihood-based inference for high-dimensional multivariate extreme value distributions, or max-stable processes, is feasible when incorporating occurrence times of the maxima; without this information, ddd-dimensional likelihood inference is usually precluded due to the large number of terms in the likelihood. However, some studies have noted bias when performing high-dimensional inference that incorporates such event information, particularly when dependence is weak. We elucidate this phenomenon, showing that for unbiased inference in moderate dimensions, dimension ddd should be of a magnitude smaller than the square root of the number of vectors over which one takes the componentwise maximum. A bias reduction technique is suggested and illustrated on the extreme-value logistic model.

KW - Estimation bias

KW - Likelihood inference

KW - Logistic model

KW - Multivariate extreme value theory

U2 - 10.1093/biomet/asv029

DO - 10.1093/biomet/asv029

M3 - Journal article

VL - 102

SP - 705

EP - 711

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 3

ER -