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dc.contributor.authorKravetc, Tatiana
dc.contributor.authorBang, Børre
dc.contributor.authorDalmo, Rune
dc.date.accessioned2017-11-29T12:11:14Z
dc.date.available2017-11-29T12:11:14Z
dc.date.issued2017-10-18
dc.description.abstractRegression analysis allows us to track the dynamics of change in measured data and to investigate their properties. A sufficiently good model allows us to predict the behavior of dependent variables with higher accuracy, and to propose a more precise data generation hypothesis. By using polynomial approximation for big data sets with complex dependencies we get piecewise smooth functions. One way to obtain a smooth spline representation of an entire data set is to use local curves and to blend them using smooth basis functions. This construction allows the computation of derivatives at any point on the spline. Properties such as tangent, velocity, acceleration, curvature and torsion can be computed, which gives us the opportunity to exploit these data in the subsequent analysis. We can adjust the accuracy of the approximation on the different segments of the data set by choosing a suitable knot vector. This article describes a new method for determining the number and location of the knot-points, based on changes in the Frenet frame. We present a method of implementation using generalized expo-rational B-splines (GERBS) for regression problems (in two and three variables) and we evaluate the accuracy of the model using comparison of the residuals.en_US
dc.descriptionAccepted manuscript version. Open Access Publishing in Springer Computer Proceedings Link to publisher's (Springer International Company) version: <a href=http://doi.org/10.1007/978-3-319-67885-6_8>http://doi.org/10.1007/978-3-319-67885-6_8</a>en_US
dc.identifier.citationKravetc T, Bang BEJ, Dalmo R: Regression analysis using a blending type spline construction. In: Floater MS, Lyche T, Mazure M, Mørken KMM, Schumaker LL. Mathematical Methods for Curves and Surfaces: 9th International Conference, MMCS 2016 Tønsberg, Norway, June 23-28, 2016 Revised Selected Papers, 2017. Springer Publishing Company p. 145-161en_US
dc.identifier.cristinIDFRIDAID 1519537
dc.identifier.isbn978-3-319-67885-6
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/11795
dc.language.isoengen_US
dc.publisherSpringer International companyen_US
dc.relation.ispartofseriesLecture Notes in Computer Science, vol. 10521en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410en_US
dc.titleRegression analysis using a blending type spline constructionen_US
dc.typePeer revieweden_US
dc.typeChapteren_US
dc.typeBokkapittelno


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