Normal Distribution
nothing is called 95% until it has been counted
Concept

Declustering — where it appears

Grouping the exceedances of a threshold into clusters and keeping one value from each, so that what is fitted behaves like independent extremes. The rule for where a cluster ends decides how many clusters there are, and a fixed run length splits every cluster whose members are not neighbours.

Named by 3 essays across one field — each of them below, with the objects they name alongside it.

Also named here as extremal index — the same set of essays touches all of them, so they are one junction rather than several.

The exceedances arrive together. 300 steps of a max-autoregression with dependence 0.75, drawn on a logarithmic scale because its marginal has no variance. The rule marks the 0.9 quantile: 30 of the 300 readings are above it and they fall into 5 clusters, the largest holding 11. The mean cluster holds 6.000, and its reciprocal — 0.167 — is the runs estimator of the extremal index, whose true value for this process is exactly 1 − 0.75 = 0.25. Every threshold method in the collection assumes exceedances are independent pieces of information; here 30 of them are 5.

The clustering the tail has

Every threshold method counts exceedances as though they were independent pieces of information, and in a dependent series they arrive in clusters. Ignoring that overstates a return level by the reciprocal of the extremal index — ×3.527 counted where the mean cluster holds four — and leaves a reported standard error 2.151 times too small.

extreme · Extremes A run length of 4 makes every exceedance its own cluster. 200 steps of a max-moving-maximum, X(t) = max(0.4·Z(t), 0.3·Z(t−6), 0.3·Z(t−12)) with unit Fréchet innovations Z, whose extremal index is exactly 0.40: one large innovation can put three readings above a threshold, 6 steps apart. The rule marks the 0.9 quantile and 19 readings clear it; 12 of the gaps between consecutive exceedances are exactly 6 steps. With a run length of 4, so that two exceedances 4 or more steps apart start separate clusters, they form 19 clusters, shaded, the largest holding 1. The runs estimator reads 1.000 against 0.40.

The run length a declustering chooses

The runs estimator of an extremal index carries a constant nobody derives. Where a cluster is a run of neighbouring exceedances the constant barely matters; where a cluster's members fall six steps apart, the estimate is 0.9069 at a run length of six and 0.3649 at seven against an index of 0.40, and a run length of four removes under a tenth of the overstatement declustering exists to remove. A rule that reads the run length off the data has the smallest worst error of the three.

extreme · Extremes How much of a return level's uncertainty is the extremal index's, by how far past the record the level is. Delta-method variance of the level from a declustered peaks-over-threshold fit, averaged over 300 records of 4,000 observations. The cluster rate's share at 1.5, 2, 5, 20, 100, 1000 periods: independent, 0.374, 0.253, 0.084, 0.027, 0.012, 0.005; runs of exceedances, θ = 0.6, 0.558, 0.396, 0.141, 0.039, 0.015, 0.006; spaced clusters, θ = 0.4, 0.845, 0.562, 0.222, 0.057, 0.019, 0.007; runs of exceedances, θ = 0.25, 0.787, 0.788, 0.327, 0.083, 0.024, 0.008.

Where the extremal index matters to a return level

A return level from a declustered fit depends on how often clusters arrive, which is the extremal index estimated from the same record. For a level reached once in two periods of a twenty-period record the index's error is a quarter to four fifths of the level's variance, and an interval that leaves it out covers 59.0% when clusters are long; put in, 90.0%. For a level reached once in a hundred periods its share is under two and a half per cent and adding it changes no interval at all — what fails there is the shape, fitted from however many clusters the dependence left: 82.3% coverage from forty-six clusters, 65.7% from nineteen.

extreme · Extremes

Named alongside it

The objects these essays reach for when they reach for this one.

Extremal indexGeneralised ParetoPeaks over thresholdReturn levelClosed formCluster sizeEffective sample sizeExceedanceFréchet lawIndependenceShape parameterStandard error

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