What's the current best wisdom on how to construct confidence bounds on something like a normal probability plot?
I recall having read a suggestion to Monte Carlo something like 201 simulated lines with the same number of points, then sort the order statistics, and plot the 6th and 196th of these. [I use 201 not 200 because quantile(1:201, c(0.025, 0.975)) = 6 and 196 while quantile(1:200, c(0.025, 0.975)) = 5.975 and 11.025.] I think I know how to do this, but before I code it, I'd like to ask two questions on this issue:
1. Where can I find this in the literature? I didn't find it where I thought it was, nor in anyplace else that seemed obvious to me, but I don't think I made it up and I'd like to give credit where credit it due.
2. Are there better alternatives available, especially if the distribution is a compound mixture that is easily simulated but not so easily characterized analytically?
Thanks,
spencer graves
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John Fox has qq.plot() in his "car" package for (not only) plotting pointwise confidence envelops into QQ-Plots.
See his books:
@BOOK{fox:1997,
author = "Fox,~J.",
title= {{Applied Regression Analysis, Linear Models, and Related Methods}},
publisher = {Sage},
address = "Thousand Oaks",
year = "1997"
}
@BOOK{fox:2002,
author = "Fox,~J.",
title= {{An R and S-PLUS Companion to Applied Regression}},
publisher = {Sage},
address = "Thousand Oaks",
year = "2002"
}
Uwe Ligges
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