Ranking — where it appears
Named by 6 essays across 2 fields — each of them below, with the objects they name alongside it.
The weight that is a vector
Two forecasts have a best combination and one number describes it. Eight have a best combination too, and the vector describing it puts nothing at all on the forecast with the smallest mean squared error.
A league table of a hundred
A hundred groups with sizes from 4 to 400, and a top ten to publish. Ranked by their own means, small groups fill 62.1% of the top ten against their 36.3% share of the true top ten. Ranked by posterior means they fill 13.4%. The ranking built from each group's chance of being in the top ten recovers 5.47 of the true ten, the best of three and barely half; and the group ranked first could hold any rank from 1 to 31.
The small groups one centre protects
In a league table of a hundred groups where larger groups do better on average, pooling every group towards one centre overstates the small groups by half a population width — 0.501 — because it pulls them towards an average they are not part of. The top ten barely notices. The bottom ten, the list that flags poor performers, holds 3.19 of the true bottom ten when pooled to one centre and 4.28 when the groups are ranked by their own noisy means. Pooling towards a centre that rises with size restores 4.35 and puts the small groups back at the bottom where 80.6% of the true bottom ten sit.
The relation the table has to estimate
Pooling a league table's groups towards a centre that rises with size repaired the small groups' bias — when the size relation was known. Fitted from the table itself, a straight line in log size loses almost nothing: the small groups' error is 0.750 against 0.719 with the true relation. When the relation bends below the median size, the fitted line extrapolates the large groups' slope into the region where it is barely observed and misplaces the small groups by −0.173 if the relation flattens and +0.264 if it steepens — smaller than one centre's error, of either sign. A quadratic in log size removes nearly all of it, −0.025 and +0.014, and costs 0.025 in error when the relation was straight.
A ranking with its uncertainty
A league table's reader wants a rank, and a rank interval inherits a fitted size relation's errors differently from an effect interval. When the relation steepens below the median size, a fitted line's 95% interval for a small group's effect covers 83.1% of the time; its 90% interval for the same group's rank covers 95.6%, because the line's error lifts the small groups into the crowded middle of the table, where rank intervals are wide. The error surfaces instead in the large groups' ranks, 87.6%, and in width: 36.9 places against the 27.3 the true relation needs. A quadratic's uncertainty has to be drawn once per table, since it moves every small group together — added group by group it over-covers at 92.8%.
A centre chosen by its own table
A league table that picks its centre's shape from its own data — a line unless a curvature test says otherwise — picks the line on 85.5% of tables whose relation flattens below the median, because the bend lives where the groups are smallest and noisiest. Its bottom ten then flags like the line's: 2.61 innocent small groups a table against 2.19 for a quadratic told the shape. Always fitting the quadratic costs 0.07 of a correctly flagged group when the relation is straight and matches the told answer when it bends, and a flag set by probability carries the choice through a mixture of the two.
Named alongside it
The objects these essays reach for when they reach for this one.
ShrinkageHierarchical modelLeague tablePartial poolingModel selectionParameter uncertaintyPosterior meanRegression to the meanAutocorrelationBenchmark forecastBias-varianceCombination weight