On two flexible methods of 2-dimensional regression analysis

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dc.contributor.authorVolf, Petr
dc.date.accessioned2017-11-02
dc.date.available2017-11-02
dc.date.issued2012
dc.description.abstractThe paper deals with the problem of non-parametric statistical modeling of 2-dimensional sur- faces from observed data, i.e. the regression analysis. In general, the model is constructed from a set of basal functions, as are the splines, gaussians and others. However, such model- ing means to estimate a large set of parameters (locations of functional units and parameters of their combination). We shall present two approaches allowing reduction of the number of needed parameters. Namely, a well known method of projection pursuit, and the less known method of Gordon surface. Further, we shall analyze possible serious consequences of sparse data to precision of model and uncertainty of prediction. Methods will be illustrated in artificial examples.en
dc.formattextcs
dc.format.extent11 stran
dc.identifier.eissn1803-9790
dc.identifier.issn1803-9782
dc.identifier.otherACC_2012_4_18
dc.identifier.urihttps://dspace.tul.cz/handle/15240/21150
dc.language.isoen
dc.licenseCC BY-NC 4.0
dc.publisherTechnická univerzita v Liberci, Česká republikacs
dc.relation.isbasedonANDĚL, J.: Foundantions of Mathematical Statistics (in Czech: Základy matematické statistiky). Matfyzpress, Praha, 2005.
dc.relation.isbasedonBISHOP, C.: Neural Networks for Pattern Recognition. Cambridge Univ. Press, Cam- bridge, 1992.
dc.relation.isbasedonDE BOOR, C.: A Practical Guide to Splines. Springer Verlag, Berlin, 1978.
dc.relation.isbasedonFRIEDMAN, J.H.: Multivariate adaptive regression splines, with Discussion and Rejoin- der. Annals Statist. 19, 1991, pp. 1–141.
dc.relation.isbasedonGAMERMAN, D.: Markov Chain Monte Carlo. Chapman and Hall, New York, 1997.
dc.relation.isbasedonGORDON, W.J.: Spline-blended surface interpolation through curve networks. Journal of Mathematics and Mechanics 18, 1969, pp. 931–952.
dc.relation.isbasedonHUBER, P.J.: Projection pursuit. Annals Statist. 13, 1985, pp. 435–475.
dc.relation.isbasedonVOLF, P.: MCMC methods of randomized optimization and data analysis. In: Proceedings of the ICPM 2007, TU Liberec, 2007, pp. 123-130.
dc.relation.isbasedonWOLD, S.: Spline functions in data analysis. Technometrics 16, 1974, pp. 1–11.
dc.relation.ispartofACC Journalen
dc.relation.isrefereedtrue
dc.subjectstatisticsen
dc.subjectregression analysisen
dc.subjectsplinesen
dc.subjectprojection pursuiten
dc.subjectGordon surfaceen
dc.subjectprediction erroren
dc.titleOn two flexible methods of 2-dimensional regression analysisen
dc.typeArticleen
local.accessopen
local.citation.epage165
local.citation.spage155
local.fulltextyesen
local.relation.issue4
local.relation.volume18
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