Smoothing noisy data using dynamic programming and generalized cross-validation.

C R Dohrmann; H R Busby; D M Trujillo

doi:10.1115/1.3108403

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Smoothing noisy data using dynamic programming and generalized cross-validation.

C R Dohrmann·H R Busby·D M Trujillo

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Abstract

Smoothing and differentiation of noisy data using spline functions requires the selection of an unknown smoothing parameter. The method of generalized cross-validation provides an excellent estimate of the smoothing parameter from the data itself even when the amount of noise associated with the data is unknown. In the present model only a single smoothing parameter must be obtained, but in a more general context the number may be larger. In an earlier work, smoothing of the data was accomplished by solving a minimization problem using the technique of dynamic programming. This paper shows how the computations required by generalized cross-validation can be performed as a simple extension of the dynamic programming formulas. The results of numerical experiments are also included.

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Algorithms Analysis of Variance Biomechanical Phenomena Data Interpretation, Statistical Models, Theoretical Numerical Analysis, Computer-Assisted Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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C R Dohrmann, H R Busby, D M Trujillo. 1988. Smoothing noisy data using dynamic programming and generalized cross-validation.. https://doi.org/10.1115/1.3108403

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