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Methods for numerical differentiation of noisy data. (English) Zbl 1287.65016
Summary: We describe several methods for the numerical approximation of a first derivative of a smooth real-valued univariate function for which only discrete noise-contaminated data values are given. The methods allow for both uniformly distributed and non-uniformly distributed abscissae. They are compared for accuracy on artificial data sets constructed by adding Gaussian noise to simple test functions. We also display results associated with an experimental data set.
Reviewer: Reviewer (Berlin)

MSC:
65D25 Numerical differentiation
65D07 Numerical computation using splines
65D10 Numerical smoothing, curve fitting
Software:
TSPACK
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