[R-sig-ME] Calculating upper and lower confidence limits on a population estimate derived from multiple point estimates

[Baldwin, Jim -FS]

I wonder if the following publication might be of use: http://www.treesearch.fs.fed.us/pubs/40477.  The paper gives conditions where the spatial covariance structure can be ignored when performing a model-based inference.  Essentially only the estimated variability of the coefficients in the prediction equation are considered (again, when certain conditions are satisfied).

Jim


-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Reuben Smit
Sent: Monday, July 28, 2014 10:05 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Calculating upper and lower confidence limits on a population estimate derived from multiple point estimates

I am generating a river reach population estimate for a freshwater mussel by summing point estimates made across a gridded point network (within the
reach) using a generalized linear mixed model framework. I have generated 95% confidence/prediction intervals at each of the ~150,000 point locations in R. I have summed all of the point estimates to derive the reach population estimate, but am unsure how to derive a single confidence interval for the population estimate using the 150,000 individual-point confidence intervals. My question: Is it statistically valid to simply sum all the lower estimates and upper estimates to obtain the absolute upper and lower most population confidence limits?

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