I am not clear wether it is allowed to start a discussion on SIC2004 before it

actually starts. Anyway I would like to promote discussion on the following

point

In one sentence: Due to game theory, one of the worst blind algorithms will

perform best in SIC2004.

The point is:

A fully automatic estimation algorithm has to obey the laws of game theory.

Especially we have the classical problem of statistical optimality:

Let L(A,P) discribe any negativ measure of fitness (The expecedt Loose in

statistics) of an Algorithm A to cope with a truth with probability

distribution P. Than in general it does not exist any algorithm A0 with

L(A0,P) <= L(A,P) forall A and P

That leads to the definition of an admissible estimator A0 in statistics which

is given by

It does not exist any A1 such that A1 is striktly better.

Not Exists A such that forall P : L(A1,P) <= L(A0,P)

Comparing to admissible estimators for P in {P0,P1} leads following

conclusion:

When L(A0,P0)<L(A1,P=) is then L(A0,P1)>L(A1,P1)

Thus the estimator performing best with that one Problem will be probabily

worse on others, because a simple/specific algorithm fit for the specific

problem will perform best. The question is just, which simple/specific

algorithm will win (because that will depend on the problem, since specific

algorithms perform best on their own problem but worse on others). But what

we need for a blind mapping is something totally different:

It should perform well for all P. (Or even better: Bail out with error

message, when it not able to give good results)

This corresponds to the concept of minmax estimators which minimize the

maximum L(A,P) or to Bayes Estimators minimizing the the mean of L(A,P) over

all expected P s.

However for any Minimax estimator typically for any fixed P a better algorithm

exists. And because many alorithms are in the test, but only one problem is in

the test, we will see one of the naive ones to perform best.

As an example compare to algorithms using oridinary kriging with Alg1: a

linear variogram, Alg2: a power variogram.

If the data ist indeed obeying a linear variogram Alg1 is BLUE and Alg2

estimates the a power near one and will be nearly BLUE. Alg1 won and Alg2 is

slightly worse.

If the data is obeying a spherical variogram, Alg2 performs better than, but

will be outnumbered by simple inverse square distances methods.

However Alg2 was performing well in both cases.

Thus I would propose to modify SIC2004 in the following way:

Give multiple problems.

Hoping for nice discussion,

Gerald

On Wednesday 14 April 2004 11:01, Gregoire Dubois wrote:

> Good day everyone!

>

> Time is ripe for a new SIC (Spatial Interpolation Comparison) exercise !

>

> The second edition of SIC (SIC2004

> <http://www.ai-geostats.org/events/sic2004.htm> ) will be launched by

> the end of this month. The topic of this year will be "automatic

> mapping", that is the use of algorithms for spatial interpolation that

> will not require any intervention or decision from the users. Hence the

> expression "one-click mapping". Such algorithms would be obviously more

> than useful in the frame of environmental monitoring networks (e.g.

> automatic mapping of ozone levels in cities, radioactivity in the

> environment, etc.). However, SIC97 has shown that it was very difficult

> to generate good results if one is not using the information provided by

> the spatial correlation (i.e. semivariograms). Can we today blindly use

> functions for the automatic fitting of semivariograms? Can machine

> learning algorithms compete with geostatistical functions?

>

> As for SIC97 (see <http://www.ai-geostats.org/events/sic97.htm>

> http://www.ai-geostats.org/events/sic97.htm ), participants to SIC2004

> will receive a subset of an environmental data set (typically

> measurements of an environmental variable + spatial coordinates of the

> sampling places) and will have to estimate the values taken by the

> variable at the remaining locations of the full data set. The true

> values found at these locations will be made public only at the end of

> the exercise. Various criteria will be used to assess the performances

> of the interpolation algorithms (time of calculation, minimum errors,

> etc.).

>

> Because everything should be automatic, participants to SIC2004 will

> have to prepare their algorithms before receiving the data: only

> sampling locations will be given and no interaction with the algorithm

> will be allowed during the exercise. No worry, participants will have

> from the end of this month until the 15th of September to setup their

> functions.

>

> Participants to SIC2004 will be invited at the end of the exercise to

> submit a manuscript for publication in the online journal GIDA

> (Geographic Information and Decision Analysis). A selected number of

> papers will be published in a book (a European Report hardcopy) with

> some unpublished material provided by the editorial board.

>

> For more information, please visit the web site

> http://www.ai-geostats.org/events/sic2004.htm

>

> Please forward this message to anyone who might be interested in this

> topic. I hope you will join numerously.

>

> Best regards,

>

> Gregoire (moderator of AI-GEOSTATS)

>

> PS: If you intend to participate, please send an email to

> <mailto:gregoire.dubois@...> gregoire.dubois@... with your name

> and your professional affiliation in the body text and the words

> "register SIC2004" in the subject of the email. An email confirming that

> you are registered will be sent back to you. No questions sent before

> Monday the 3rd of May 2004 about the exact content/purpose of SIC2004

> will be answered.

>

>

>

> __________________________________________

> Gregoire Dubois (Ph.D.)

> JRC - European Commission

> Radioactivity Environmental Monitoring

> TP 441, Via Fermi 1

> 21020 Ispra (VA)

> ITALY

>

> Tel. +39 (0)332 78 6360

> Fax. +39 (0)332 78 5466

> Email: gregoire.dubois@...

> WWW: http://www.ai-geostats.org <http://www.ai-geostats.org/>

> WWW: http://rem.jrc.cec.eu.int <http://rem.jrc.cec.eu.int/>

--

-------------------------------------------------

Prof. Dr. K. Gerald v.d. Boogaart

Professor als Juniorprofessor für Statistik

http://www.math-inf.uni-greifswald.de/statistik/

office: Franz-Mehring-Str. 48, 1.Etage rechts

e-mail: Gerald.Boogaart@...

phone: 00+49 (0)3834/86-4621

fax: 00+49 (0)89-1488-293932 (Faxmail)

fax: 00+49 (0)3834/86-4615 (Institut)

paper-mail:

Ernst-Moritz-Arndt-Universität Greifswald

Institut für Mathematik und Informatik

Jahnstr. 15a

17487 Greifswald

Germany

--------------------------------------------------

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