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.weyla...@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....@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. > [[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 and provide commented, minimal, self-contained, reproducible code.
