Hi Michael,

Thank you for your help!

I did some googling and researching... Reading the following article,
http://www.ecd.bnl.gov/pubs/BNL-79819-2008-JA.pdf
It seems that once we estimate the parameters of the bivariate normal
distribution,
then we can plug into the formula of conditional distribution of
Y|X=x1+x2+x3 ?
http://en.wikipedia.org/wiki/Multivariate_normal_distribution
My question is:
Is it a correct procedure to do the following:
Step 1: estimate the parameters of the bivariate normal distribution;
Step 2: plug the estimated parameters into the Y|X=x1+x2+x3 formula and get
the 95% quantile of it?
Do I need to repeat Step 2 many times following the bootstrapping procedure?
Or one shot of Step 2 is enough?
I got very much confused...
Any thoughts?
Thanks a lot!
On Wed, Apr 11, 2012 at 10:12 AM, R. Michael Weylandt <
michael.weylandt@gmail.com> wrote:

> Given the caveats Ted describes here:
> http://tolstoy.newcastle.edu.au/R/help/05/06/5992.html it seems that
> bootstrapping might be the only way to get (somewhat) credible
> prediction intervals: the boot package on CRAN can help to facilitate
> getting these. Here's some documentation for CI:
>
> http://www.statmethods.net/advstats/bootstrapping.html
>
> but you'll need to adopt it for a prediction interval, which might
> entail hacking boot.ci().
>
> You might also see if this question, by someone who most certainly
> isn't you because cross-posting is discouraged, gets some answers:
>
> http://stats.stackexchange.com/questions/26277/how-to-bootstrap-prediction-intervals-for-customized-regression-models-in-r
>
> Michael Weylandt
>
> On Wed, Apr 11, 2012 at 10:29 AM, Michael <comtech.usa@gmail.com> wrote:
> > Hi all,
> >
> > Are there functions in R that could help me do the following?
> >
> > We have a special type of regression which is called Geometric Mean
> > Regression.
> >
> > We have done some search and found the following:
> >
> > https://stat.ethz.ch/pipermail/r-help/2011-July/285022.html
> >
> > The question is: how to do the statistical inference on GMR results?
> >
> > More specifically, we are looking for the prediction interval:
> >
> > Lets say we regress y1, y2, ..., yn onto x1, x2, ..., xn:
> >
> > we would like to know what's the prediction interval for a new data
> point:
> >
> > x_new=x1+x2+x3
> >
> > (i.e. the new data point is the sum of the existing first three data
> points)
> >
> > In ordinary linear regression, we could derive prediction interval for an
> > in-sample data point as well as a new data point...
> >
> > For our x_new=x1+x2+x3, we can derive formulas for the prediction
> interval.
> >
> > But for the above customized regression,
> >
> > how do we obtain the prediction intervals?
> >
> > ------------------------------
> >
> > Are there functions in R that can help us do this?
> >
> > We are thinking of using bootstrapping, etc. Are there functions in R
> help
> > us on this?
> >
> > Thanks a lot!
> >
>  >        [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
> > and provide commented, minimal, self-contained, reproducible code.
>

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