7925 5 5197 1 13122 document 5197 XJ-06-012.pdf application/pdf 2f212e6f0fd1a5fce667846cbbb212a2 MD5 81867 2016-01-15 02:19:16 https://pure.iiasa.ac.at/id/eprint/7925/1/XJ-06-012.pdf 7925 1 1 text en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/5197 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/5197 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/5197 archive 351 disk0/00/00/79/25 2016-01-15 02:19:16 2021-08-27 17:19:29 2016-01-15 02:19:16 article show 3 Fischer G. 276 Ermolieva T. 7638 Ermoliev Y. 1445 van Velthuizen H.T. 7495 pub iis_luc prog_luc In this paper, we propose a downscaling procedure that provides a basis for recovery and estimation of incomplete, aggregate, unknown or indirectly measurable variables. It makes maximum use of information and dependencies on various levels relying on the cross-entropy maximization principle. We show that the maximum entropy principle can be viewed as the extension of the maximum likelihood principle. In this sense, the convergence of the proposed downscaling methods to solutions maximizing an entropy function can be considered as an analog of the asymptotic consistency analysis in traditional statistical estimation theory. The main motivation for the development of the procedure has been a practical example of spatial estimation of agricultural production values. We briefly discuss the main challenges related to the choice of priors (location specific information) and their inherent uncertainties that to large extent determine the success of the downscaled results. 2006 published Information Resources Management Association XJ-06-012 International Journal of Knowledge and Systems Sciences; 3(1): [2006] [ISBN4-924861-09-X] 89 83 338 349 public International Journal of Knowledge and Systems Sciences 3 1 TRUE 4-924861-09-X 1947-8208 http://www.jaist.ac.jp/library/jaist-press/eng/catalogue/kss2005.html pub FALSE info:eu-repo/semantics/article