not sure if anyone included this reference as a response, but here is a
paper by Pierre Goovaerts and Hirotaka Saito, 2000, Geostatistical
interpolation of positively skewd and censored data in a Dioxin contaminated
site, Environ, Sci, Technol. 34, 4228-4235,
Geostatistics - Environmental Science - Industrial Ecology
Centre for the Management of Environmental Resource(CMER)
Boulevard de Constance,
77305 Fontainebleau Cedex,
Tel: 33 (0)1 60 72 4456
Fax: 33 (0)1 60 74 55 64
> -----Original Message-----http://www.netaddress.com/?N=1
> From: Gregoire Dubois [mailto:gregoire.dubois@...]
> Sent: Sunday, January 07, 2001 2:36 PM
> To: Berterretche Mercedes
> Cc: ai-geostats@...
> Subject: Re: [AI-GEOSTATS: MSE to compare different methods]
> Dear Mercedes,
> doing k fold cross validation (taking out X % of the samples)
> will not give
> you any reliable results unless you repeat the operation
> several times. Taking
> out 15% of the samples one time only will give you an MSE
> that will depend
> strongly on the data you have removed. Has the selection of
> the 15% been made
> randomly? You may get a strong bias if the 15% of the samples
> have been taken
> in one region in particular or if you have taken out extreme
> values only. At
> this stage, I would trust more the results obtained by standard cross
> validation (leave one out method).
> I didn't check your previous mail but if you have few samples only,
> k-fold cross validation won't help you much.
> If you have many samples, then you should repeat the
> procedure at least 10
> times to be sure that the way you have extracted the data
> has not influenced
> too much the results.
> Also, if you have a phenomenon that fluctuates at different
> scales, you may
> have removed the short scale effect by taking out only few
> samples (15% is not
> My suggestion is the following: it is time consuming but
> might be worth the
> effort. The idea is to take out an increasing number of
> samples (10, 20, 30,
> 40, 50, 60, ...,X%) of samples, this 10 times, and see how
> the average MSE
> evolves. You may find out that methods A & B work better than
> C & D when only
> few samples are removed and that C & D give better results
> than A & B when
> more than 40% of the samples have been removed. This would
> mean that C & D
> describe better the general trend of the phenomenon while A
> & B are more
> sensitive to the local structures (since you have more dense data).
> If you don't have the time to proceed in such a way, you
> should use standard
> cross validation only and investigate the regions/samples
> where you have the
> highest errors.
> Just few thoughts.
> "Berterretche, Mercedes" <Mercedes.Berterretche@...> wrote:
> > I would like to thank Benjamin Warr for his siggestion about doing
> > difference images instead of global measures as MSE.
> > I'm confused because crossvalidation MSE (taking one sample out and
> > recalculating) and validation MSE (taking 15 percent of the
> samples out and
> > recalculating) are giving me opposite results. The
> validation method would
> > allows me to compare kriging vs cokriging vs Kriging with
> an external drift
> > vs regression , but I don't know if I can trust the results
> at this point.
> > Does anybody have any input about this?
> > Thanks in advance,
> > Mercedes Berterretche
> > --
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> Gregoire Dubois (Ph.D.)
> Institute of Mineralogy and Petrography
> Dept. of Earth Sciences
> University of Lausanne
> Get free email and a permanent address at
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