# RE: [R] bootstrap vs. resampleing

From: Huntsinger, Reid <reid_huntsinger_at_merck.com>
Date: Thu 07 Apr 2005 - 07:48:30 EST

I may be misunderstanding the question, but I believe you want a pointwise confidence band for the conditional odds function. The issue here is less bootstrap versus some other resampling plan, and more how to do it at all. For example, if no matter what "training" data you feed in, you always get the same conditional odds estimate, no resampling will (by itself) reveal this bias (and you will have a confidence band of width 0). You could however use resampling together with nonparametric estimation in a variety of ways to address this.

If you assume your conditional odds estimation to be unbiased, you could resample and look at the empirical distribution of conditional odds ratio estimates at a given covariate or feature value. You have to figure out how this is related to the population distribution; this is easiest with the bootstrap since you have the same sample size. In this case the simplest procedure is to treat the bootstrap distribution as the population distribution, but there are many alternatives. See the book Thomas Lumley recommended by Jun Shao and Dongsheng Tu. They treat estimation of regression functions in several places; those remarks are relevant for your case as well.

Reid Huntsinger

Hi,

I understand bootstrap can be used to estimate 95% confidence interval for some statistics, e.g. variance, median, etc. I have someone suggesting that by resampling certain proportion of the total samples (e.g. 80%) without replacement, we can also get the estimate of confidence intervals. Here we have an example of 1000 obsevations, we would like to estimate 95% confidence intervals for odds ratio for a diagnostic test, can I use resampling 80% of the observations without replacement, instead of bootstrap, to do this? If not, why is it wrong to do it this way?

Thanks

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