Re: [R] Coefficients of Logistic Regression from bootstrap - how

From: Ted Harding <Ted.Harding_at_manchester.ac.uk>
Date: Mon, 21 Jul 2008 21:11:13 +0100 (BST)


There is one aspect for which bootstrap or re-sampling is useful, which is not provided by maximum likelihood estimation (and the usual MLE estimates of SEs of the coefficients.

That is, that the SEs of the coefficients are conditional on the values of the covariates in the sample. The only random variation that is considered in producing the SEs in standard regression is that of the response variable, as implied by the model being fitted.

Hence the MLE will tell you about the uncertainty in the coefficients due to random response, but with only the exact covariate values which are present in the sample.

In practice, as has been indicated by other responses, the data are from a population in which the covariates vary and not all have been observed, and there is interest in assessing the uncertainty about the "population coefficients" due to this.

An indication of this (with somewhat uncertain reliability) can be obtained by a bootstrap procedure, on the basis that sampling from the sample will have some resemblance to sampling from the population.

Ted.

On 21-Jul-08 19:56:16, wrote:
> 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......
>
> Thanks,
>
> --Jerry
> 2008/7/21 Doran, Harold <HDoran_at_air.org>:
>

>> > 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.
>>
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>
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>
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E-Mail: (Ted Harding) <Ted.Harding_at_manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861
Date: 21-Jul-08                                       Time: 21:11:10
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