Least-squares fitting algorithms of the NIST algorithm testing system
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Least-Squares Fitting Algorithms of the NIST Algorithm Testing System
This report describes algorithms for fitting certain curves and surfaces to points in three dimensions. All fits are based on orthogonal distance regression. The algorithms were developed as reference software for the National Institute of Standards and Technology's Algorithm Testing System, which has been used for 5 years by NIST and by members of the American Society of Mechanical Engineers' ...
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Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...
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Least-squares fitting, first developed by Carl Friedrich Gauss, is arguably the most widely used technique in statistical data analysis. It provides a method through which the parameters of a model can be optimised in order to obtain the best fit to a data set through the minimisation of the squared differences between the model and the data. This tutorial document describes the closely associa...
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Multiharmonic least-squares fitting algorithms have been applied in many fields of instrumentation and measurement. However, as the number of harmonics increases, the size of the matrices needed to describe the relation between the sampled signal and the multiharmonic model increases substantially. In this paper an efficient memory-wise implementation of the algorithm is proposed, and numerical...
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ژورنال
عنوان ژورنال: Journal of Research of the National Institute of Standards and Technology
سال: 1998
ISSN: 1044-677X
DOI: 10.6028/jres.103.043