Ive been a lurker for a while, and have learned a lot from reading the

discussions, so thanks in advance for that.

My question concerns the use of the normal score transform when making

repeated conditional sequential Gaussian simulations using GSLIB. I believe

the criticism that the backtransform would give biased results (as discussed

in the Saito and Goovaerts 2000 paper in the discussion about Multi-Gausinan

Kriging) does not apply to simulations because at each point to be

simulated, a single normal score value is drawn at random from the cdf

obtained by kriging. The averaging takes place in the original data space. I

came to this conclusion from trying to figure out how I could apply the

correction described in the Saito and Goovaerts paper.

But even if the above is true, I may still have a problem because of the

high percentage of zeros in my data sets, which ranges from 4 to 22%. I

(the GSLIB program actually) rank these zero values randomly and I dont

know how to implement the suggestion (of Goovaerts, citing Verly 1986) of

ranking them based on the average value in a search radius so that zeros

near high densities have higher ranks than those in low density areas. For

my purposes, I calculate the total abundance for each realization, and use

the frequency distribution of these totals to calculate empirical confidence

intervals, so Im mostly interested in the variability in these total

abundance realizations. How would the zeros affect this? Someone has

suggested that doing the ranking randomly would increase the nugget effect

of the normal score variograms. However, I have 6 data sets and the ones

with the highest % of zeros are not the ones with the largest nuggets. If

the nugget has been artificially inflated because zeros are not correlated

after nscore transform when in fact they are correlated in the raw data

space, is it reasonable to say that the variability of the simulated total

abundances would be overestimated (and thus conservative)?

Cheers,

Paul Walline

NOAA Fisheries, Alaska Fisheries Science Center