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ISJ Intelligent Mathematics

Article ID: 146

Received: 2016-10-23
Check for Plagiarism: 2016-12-01
Approved by Editors: 2016-12-15

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Article type: Short Note
Abstract: “Weather is important but hard to predict”—lay people and scientists alike will agree. The complexity of system limits the knowledge about it and therefore its predictability even over a few days. It is complex because many variables within the Earth’s atmosphere, such as temperature and they do so nonlinearly. B-spline as a basis for one-dimensional regression and we extend this paper by using B-spline to construct a basis for bivariate regression. This construction gives a basis in two dimensions with local support and hence a fully flexible family of fitted mortality surfaces one of the principal motivations behind the use of B-spline as the basis of regression is that it does not suffer from the lack of stability that can so bedevil ordinary polynomial regression. The essential difference is that B-spline have local non-zero support in contrast to the polynomial basis for standard regression. The optimal B-Spline models rely on the optimal knots that has a minimum Generalized Cross Validation (GCV)
Key words: Temperature, B-Spline, Generalized Cross Validation, non-parametric

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