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

Likelihood — where it appears

The probability a model assigns to the data that were observed, read as a function of the model's parameters. Comparing it between models requires every term of it, including any factor a transformation of the sample introduced — which is exactly the term a criterion reading a whitened residual sum leaves out.

Named by 4 essays across 4 fields — each of them below, with the objects they name alongside it.

The term that cancels, and the term that does not. The volume each candidate's whitening moves — log|Ω̂| — for a sieve of order 4 on one sample of 120 rows. Estimated once from the fullest candidate and used for the whole table, it is the same number for every candidate, so it drops out of every difference the criterion reads: that is why nothing in this collection has ever needed to carry it. Estimated from each candidate's own residuals it ranges over 23.87, which is more than a parameter is worth, and the criteria being compared are then fits made under different error models with no term saying so. The window's rule has carried this term since the estimated-covariance field and the sieve's never had it.

The volume a whitening moves

A sieve's whitening has a determinant and this collection's criterion for it never carried one. Shared across a table the term cancels exactly, which is why nothing ever noticed; used per candidate it is worth more than a parameter and the whole comparison turns on it.

lists · Criterion A window with two wrong ends. The regret of a rule whitened by a Bartlett-tapered Ω̂, as the window widens, against three rules that need no window at all. At L = 0 the estimate is the identity and the rule is exactly least squares — 0.08556, the same number to five places. It falls to 0.01754 at L = 20 and rises again by L = 30, because a quarter of the sample's lags are then being estimated from it. The automatic bandwidth a practitioner would reach for, 4(n/100) to the power 2/9, which at this sample size is 4, gives 0.02963 — 69% above the best available window. The rule told the dependence is an AR(1) sits at 0.01440 throughout, which is the price of not knowing the form.

The window that has to be chosen, and the term that was dropped

An estimated covariance has a bandwidth in it, and both ends of the dial are wrong for different reasons. The rule a practitioner would reach for is two thirds worse than the best window there is.

banded · Order selection The interval every package reports first does not cover. Counted coverage of two 95% intervals for the 100-block return level of a normal parent, against the length of the record they were fitted from, over 300 records at each length. The level they are about is known in closed form, so this is coverage of a number rather than agreement between two estimates. The delta-method interval covers 80.3% at 25 blocks and reaches only 89.0% at 200; the profile-likelihood interval sits between 94.0% and 94.7% throughout. The gap is not a small-sample effect that lengthening the record removes — it narrows by 8.7 points for an eightfold longer record.

Two intervals for one return level

Two 95% intervals read off the same fits of the same records, against a level known in closed form. The symmetric one covers 80.3% at twenty-five blocks and reaches only 89.0% at two hundred — and 99.24% of its misses are the interval sitting entirely below the truth, which is not the endpoint anybody expects to fail.

extreme · Extremes The shape one real modifier leaves on a table of eight characteristics, and one trial's table. Expected interaction z when characteristic 1 modifies the effect at h = 1: 2.80, 2.00, 2.00, 2.00, 0.00, 0.00, 0.00, 0.00 — the real row 2.80, its three nested neighbours 0.713 of that, the four unrelated characteristics nothing. One seeded trial of 400 reads 1.91, 1.90, 2.06, 1.95, 0.14, 0.33, 0.39, 2.08.

The pattern a cause leaves on its neighbours

A characteristic that modifies a treatment's effect raises every characteristic that overlaps it, in proportion to the overlap, and fitting that pattern was supposed to say which one is the cause. Fitted with the noise's own correlation it names the largest row on every trial, because the noise draws the same pattern — so the 54.4% at which the largest row is right among nested characteristics is the most any reading of the table can manage, and the honest answer is a set of about five.

alongside · Repetition

Named alongside it

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

Closed formCovariance matrixDegrees of freedomDependenceGeneralised least squaresInformation criterionModel selectionNuisance parameterAutocorrelationAutoregressionBlock maximaCholesky

All concepts