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Fast Compact Algorithms and Software for Spline Smoothing [electronic resource] / by Howard L. Weinert.

By: Weinert, Howard L [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: VIII, 45 p. 7 illus., 5 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461454960.Subject(s): Statistics | Computer mathematics | Computer software | Applied mathematics | Engineering mathematics | Statistics | Statistics and Computing/Statistics Programs | Signal, Image and Speech Processing | Computational Science and Engineering | Appl.Mathematics/Computational Methods of Engineering | Mathematical SoftwareAdditional physical formats: Printed edition:: No titleDDC classification: 519.5 Online resources: Click here to access online
Contents:
Introduction -- Cholesky Algorithm -- QR Algorithm -- FFT Algorithm -- Discrete Spline Smoothing.
In: Springer eBooksSummary: Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.
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Introduction -- Cholesky Algorithm -- QR Algorithm -- FFT Algorithm -- Discrete Spline Smoothing.

Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.

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