Fast Compact Algorithms and Software for Spline Smoothing [electronic resource] /
Weinert, Howard L.author.
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.
Description based upon print version of record.
Includes bibliographical references.
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.
Statistics .
Signal processing.
Image processing.
Speech processing systems.
Computer mathematics.
Applied mathematics.
Engineering mathematics.
Computer software.
Statistics and Computing/Statistics Programs.
Signal, Image and Speech Processing.
Computational Science and Engineering.
Mathematical and Computational Engineering.
Mathematical Software.