On two flexible methods of 2-dimensional regression analysis

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dc.contributor.author Volf, Petr
dc.date.accessioned 2017-11-02
dc.date.available 2017-11-02
dc.date.issued 2012
dc.identifier.issn 1803-9782
dc.identifier.other ACC_2012_4_18
dc.identifier.uri https://dspace.tul.cz/handle/15240/21150
dc.description.abstract The 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.format text cs
dc.format.extent 11 stran
dc.language.iso en
dc.publisher Technická univerzita v Liberci, Česká republika cs
dc.relation.ispartof ACC Journal en
dc.relation.isbasedon ANDĚL, J.: Foundantions of Mathematical Statistics (in Czech: Základy matematické statistiky). Matfyzpress, Praha, 2005.
dc.relation.isbasedon BISHOP, C.: Neural Networks for Pattern Recognition. Cambridge Univ. Press, Cam- bridge, 1992.
dc.relation.isbasedon DE BOOR, C.: A Practical Guide to Splines. Springer Verlag, Berlin, 1978.
dc.relation.isbasedon FRIEDMAN, J.H.: Multivariate adaptive regression splines, with Discussion and Rejoin- der. Annals Statist. 19, 1991, pp. 1–141.
dc.relation.isbasedon GAMERMAN, D.: Markov Chain Monte Carlo. Chapman and Hall, New York, 1997.
dc.relation.isbasedon GORDON, W.J.: Spline-blended surface interpolation through curve networks. Journal of Mathematics and Mechanics 18, 1969, pp. 931–952.
dc.relation.isbasedon HUBER, P.J.: Projection pursuit. Annals Statist. 13, 1985, pp. 435–475.
dc.relation.isbasedon VOLF, P.: MCMC methods of randomized optimization and data analysis. In: Proceedings of the ICPM 2007, TU Liberec, 2007, pp. 123-130.
dc.relation.isbasedon WOLD, S.: Spline functions in data analysis. Technometrics 16, 1974, pp. 1–11.
dc.subject statistics en
dc.subject regression analysis en
dc.subject splines en
dc.subject projection pursuit en
dc.subject Gordon surface en
dc.subject prediction error en
dc.title On two flexible methods of 2-dimensional regression analysis en
dc.type Article en
dc.relation.isrefereed true
dc.identifier.eissn 1803-9790
local.relation.volume 18
local.relation.issue 4
local.citation.spage 155
local.citation.epage 165
local.access open
local.fulltext yes en
dc.license CC BY-NC 4.0


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