Re: [R] Coefficients of Logistic Regression from bootstrap - how to get them?

From: Tim Hesterberg <>
Date: Mon, 28 Jul 2008 04:13:28 +0200

I'll address the question of whether you can use the bootstrap to improve estimates, and whether you can use the bootstrap to "virtually increase the size of the sample".

Short answer - no, with some exceptions (bumping / Random Forests).

Longer answer:
Suppose you have data (x1, ..., xn) and a statistic ThetaHat, that you take a number of bootstrap samples (all of size n) and let ThetaHatBar be the average of those bootstrap statistics from those samples.

Is ThetaHatBar better than ThetaHat? Usually not. Usually it is worse. You have not collected any new data, you are just using the existing data in a different way, that is usually harmful: * If the statistic is the sample mean, all this does is to add   some noise to the estimate
* If the statistic is nonlinear, this gives an estimate that   has roughly double the bias, without improving the variance.

What are the exceptions? The prime example is tree models (random forests) - taking bootstrap averages helps smooth out the discontinuities in tree models. For a simple example, suppose that a simple linear regression model really holds:

        y = beta x + epsilon
but that you fit a tree model; the tree model predictions are a step function. If you bootstrap the data, the boundaries of the step function will differ from one sample to another, so the average of the bootstrap samples smears out the steps, getting closer to the smooth linear relationship.

Aside from such exceptions, the bootstrap is used for inference (bias, standard error, confidence intervals), not improving on ThetaHat.

Tim Hesterberg

>Hi Doran,
>Maybe I am wrong, but I think bootstrap is a general resampling method which
>can be used for different purposes...Usually it works well when you do not
>have a presentative sample set (maybe with limited number of samples).
>Therefore, I am positive with Michal...
>P.S., overfitting, in my opinion, is used to depict when you got a model
>which is quite specific for the training dataset but cannot be generalized
>with new samples......
>2008/7/21 Doran, Harold <>:
>> > I used bootstrap to virtually increase the size of my
>> > dataset, it should result in estimates more close to that
>> > from the population - isn't it the purpose of bootstrap?
>> No, not really. The bootstrap is a resampling method for variance
>> estimation. It is often used when there is not an easy way, or a closed
>> form expression, for estimating the sampling variance of a statistic. mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Mon 28 Jul 2008 - 02:16:49 GMT

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