Ladder

Leverage — the ladder

One essay so far against this idea. A ladder is the distinct arguments that stand against one idea, and this one has room to grow.
  1. the diagonal of the hat matrix, hᵢ = aᵢᵀ(AᵀA)⁻¹aᵢaverage p/m = 0.20010the leave-one-out residual: eᵢ/(1 − hᵢ), and forty refitsbars: closed form · dots: refitted without that pointone number, two fieldsΣ hᵢ, exactly p10largest leverage0.5closed form against refits8.9·10⁻¹³1 − h of the first row0.5y appears in the residualand nowhere in the leverage

    Influence is decided before the data

    The diagonal of the hat matrix sums to the number of columns and the response appears nowhere in it, so a fit has exactly p units of influence to hand out among m observations. The same row at h = 0.5 is a ten-fold outlier on one design and a boundary case on another, and which of those it is was settled before a single measurement was taken.

    rung 1 · leastsquares

All ladders