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:
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:
(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!
I did some googling and researching... Reading the following article,
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 ?
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...
Thanks a lot!