Ciao Simone, I'm not sure I fully understand your question. Are you referring to the monomials used to define the stationary trend model of the IRF-k? What are these coefficients of 1 and -1 ?? I must admit I am very lazy tonight !
The fundamental differences between IRF-k and UK can be found in part 6 of a paper by Arianna Orasia http://w3.uniroma1.it/DSPSA/Rapporti_tecnici/orasijonaferrari.pdf Some readings are: 1) the paper of D. Marcotte, M. David (1988) Trend surface analysis as a special case of IRF-k kriging, Mathematical Geology, 20(7): 821 - 824 2) the papers of van den Boogaart (hard reading !!!), e.g. http://www.math-inf.uni-greifswald.de/~boogaart/Publications/BoogaartPub lications.html Why Universal Kriging is better than IRFk-Kriging: Estimation of Variograms in the Presence of Trend Karl Gerald van den Boogaart& Alexander Brenning June 30, 2001 3) If you survived the reading above, then you can always go for a paper from Alexander Brenning: http://www.geographie.uni-erlangen.de/abrenning/lit/trend.pdf 4) This one should saves your day (in Italian and easy reading) but it is probably impossible to find today: Bruno R. & Raspa G. (1994). La pratica della geostatistica lineare. Il trattamento dei dati spaziali. Guerini Studio. 170 p The book is all about FAIK (Italian equivalent to IRFk) and has (had?) an MS DOS software for FAIK, FAIPACK, that was not bad at all. There was also a wonderful text from Chauvet that I could try to find again if needed but it is in French (try to contact Fontainebleau). Chiles and Delfiner probably discuss this as well in their book but I don't have it here. For what concerns the previous mail regarding trend modelling, IRF-k can be nicely automated for detecting the optimal "k" but I would make the same critic as for the Neural Nets, you do not extract any useful information from the drift (although you could map the local values of k.. allowing you so to describe somehow the complexity of the underlying trend.. Don't know if this has been done or any useful). Cheers, GD PS: there are some nice papers from Chauvet (Fontainebleau) that I could try to find again if needed but there are in French. Chiles and Delfiner probably discuss this in their book but I don't have it here. -----Original Message----- From: Simone Sammartino [mailto:[EMAIL PROTECTED] Sent: 29 June 2005 17:49 To: Geostat newsgroup Subject: [ai-geostats] Irf-k theory... Dear all about Irf-k... I can't understand the link between the two approach explaining the Irf-k theory... 1st approach: The Irf-k approach is based on a generalization of the intrinsic model, in which the coefficients of the combination of the variables in the X points are such that the combination of monomials of the variables (computed with such coefficients) is zero mean and second order stationary. In the intrinsic model the coefficients are 1 and -1 and the linear combination of the variables with such coefficients (the increment) is second order stationary. Now...how to insert the generalized covariance in this speech?... 2nd approach: The non stationary random function is seen as the sum of a trend and a residual (as in UK?!)...such trend is the linear combination of monomials of the variables (as in UK?!) with the said coefficients. And the generalized covariance? Reassuming...I did not understand the Irf-K theory... Someone can explain it to me? or suggest some clear refer? Thank you Simone ----------------------------- Dr. Simone Sammartino PhD student - Geostatistical analyst - G.I.S. mapping I.A.M.C. - C.N.R. Geomare-Sud section Port of Naples - Naples [EMAIL PROTECTED] ----------------------------- ____________________________________________________________ 6X velocizzare la tua navigazione a 56k? 6X Web Accelerator di Libero! Scaricalo su INTERNET GRATIS 6X http://www.libero.it
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