# Stat 5601 (Geyer) Examples (Subsampling Bootstrap Confidence Intervals)

## General Instructions

To do each example, just click the "Submit" button. You do not have to type in any R instructions or specify a dataset. That's already done for you.

## Introduction

Now that we have some idea of how (Efron style) bootstrap confidence intervals work, we can return to the subsampling bootstrap and look at the kind of confidence intervals Politis and Romano (1994) recommend for the subsampling bootstrap.

Somewhat suprisingly, they recommend the kind of intervals that Efron and Tibshirani disparage in Section 13.4 titles Is the Percentile Interval Backwards?. This section is a response to the recommendations of Hall (The Bootstrap and Edgeworth Expansion, Springer, 1992, and earlier papers) where he charactizes bootstrap percentile intervals as looking up [in] the wrong statistical tables backwards.

We take no sides on the argument between Efron and Hall. There are arguments on both sides, and either method performs well in some examples and badly in others.

However, for the subsampling bootstrap, there seems to be no dispute. The standard method for the subsampling bootstrap is very much like what Hall recommends for the ordinary bootstrap.

This method is explained in a handout (which will be handed out in class). If you want to see what it looks like in various formats

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## Time Series

• Everything down to the bottom of the for loop should be familiar, just like what we did when we previously visited this example except some plot commands have been removed.

• The four lines below the `for` calculate the subsampling bootstrap confidence interval. The example could have stopped there.

• The histogram shows the distribution which the subsampling bootstrap uses to calculate critical values. And the confidence interval below gives what one could do if one wanted to assume the asymptotic distribution is normal.

• Note that the histogram is quite skewed. Hence we get a confidence interval that is not symmetric about `theta.hat`. Thus we do much better by not assuming normality. We get a shorter confidence interval with better coverage.

• As usual, Efron and Tibshirani are using a ridiculously small sample size in this toy problem. There is no reason to believe the subsampling bootstrap here. But it is reasonable for (much) larger data sets. (This comment repeated from the previous discussion of the subsampling bootstrap).

## External Data Entry

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• The code immediately following the `for` loop calculates a two-sided confidence interval as described in the handout and in Politis and Romano (1994). That is what you would usually do.