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Linear problems for univariate functions with noisy data

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Average-Case Analysis of Numerical Problems

Part of the book series: Lecture Notes in Mathematics ((LNM,volume 1733))

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2.1. Notes and References

  • Plaskota, L. (1992), Function approximation and integration on the Wiener space, J. Complexity 8, 301–321.

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  • Plaskota, L. (1996), Noisy information and computational complexity, Cambridge Univ. Press, Cambridge. p. 195

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  • Plaskota, L. (1992), Function approximation and integration on the Wiener space, J. Complexity 8, 301–321.

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  • Ritter, K. (1996b), Almost optimal differentiation using noisy data, J. Approx. Theory bf 86, 293–309.

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3.1. Notes and References

  • Ritter, K. (1996b), Almost optimal differentiation using noisy data, J. Approx. Theory bf 86, 293–309.

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  • Micchelli, C. A. (1976), On an optimal method for the numerical differentiation of smooth functions, J. Approx. Theory 18, 189–204.

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  • Donoho, D. L. (1994), Asymptotic minimax risk for sup-norm loss: solution via optimal recovery, Probab. Theory Relat. Fields 99, 145–170.

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Authors
  1. Klaus Ritter
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Klaus Ritter

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© 2000 Springer-Verlag

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Ritter, K. (2000). Linear problems for univariate functions with noisy data. In: Ritter, K. (eds) Average-Case Analysis of Numerical Problems. Lecture Notes in Mathematics, vol 1733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0103939

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  • DOI: https://doi.org/10.1007/BFb0103939

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  • Print ISBN: 978-3-540-67449-8

  • Online ISBN: 978-3-540-45592-9

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