Howard L. Weinert Book order

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.

Fixed-interval smoothing is a technique designed to extract valuable insights from imprecise data, making it applicable across various fields. The method has been utilized in engineering, physical sciences, and social sciences, addressing challenges in control systems, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. Its versatility highlights its significance in enhancing data reliability and interpretation across diverse disciplines.