In article <LUxI6.945$[EMAIL PROTECTED]>,
Michael Robbins <[EMAIL PROTECTED]> wrote:
>I am fooling around with a paper that talks about how to "do inferences, like
>constructing confidence intervals, with the bootstrap method for inference...
>because the assumption of i.i.d erros is reasonable... also... it is unlikely
>that the cumulative distribution functions of our estimators are approximately
>normal."
>He also says "... we have to ensure that the residual errors are not correlated.
>If the errors exhibit some correlation, then a transformation of the residuals
>is in order."
It is rare that anything other than drastic transformations
of the residuals will remove correlation, and even if they
do, the dependence is not affected at all. Dependence, in
a model which assumes independence, means that there is
something wrong with the model. Also, one of the important
properties of the bootstrap is that normality is made far
less important.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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