A Brief Discourse on the Jackknife Method

I often use bootstrapping when I need to get an estimate of some parameter, but that is because I have access to sufficient computational resources to to just do run my analysis as much as I want. But that has not always been the case and the usual method before boot strap was jackknife. The jackknife approach is a simple leave-one-out approach, so that given [latex]n[/latex] observations, then for [latex]i = 1, 2, \ldots, n[/latex], calculate the statistics with the [latex]i[/latex]th element omitted.

There are some natural advantages to this. The most important of them is that it is relatively easy to code. An implementation in R would be only 5-6 lines of code. Another advantage is that it is easy to show the outcome’s validity. For instance, if used to calculate the mean of a dataset, it is trivial to prove that the result is the same as the mean calculated the straightforward way. Other statistics, while not necessarily resulting in the same answer, can be shown to be valid estimators, as well.

There is a downside to this, of course. For larger datasets, it can be time prohibitive to use jackknife. After all, it must find [latex]n[/latex] estimates for[latex]n[/latex] observations. If [latex]n = 1000[/latex], this is not a big deal. But if [latex]n[/latex] is in the hundreds of thousands and the statistic being calculated in relatively intense, like ordinary least squares, that is a lot of computing to do. But even then, this is not all bad. The task is embarrassingly parallel since individual calculations of the statistic are uncoupled with each other.

But I am lazy, and I will probably stick to bootstrap.