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Comparingparameter choice methods for regularization of ill-posed problems

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  • Bauer, Frank
  • Lukas, Mark A.

Abstract

In the literature on regularization, many different parameter choice methods have been proposed in both deterministic and stochastic settings. However, based on the available information, it is not always easy to know how well a particular method will perform in a given situation and how it compares to other methods. This paper reviews most of the existing parameter choice methods, and evaluates and compares them in a large simulation study for spectral cut-off and Tikhonov regularization. The test cases cover a wide range of linear inverse problems with both white and colored stochastic noise. The results show some marked differences between the methods, in particular, in their stability with respect to the noise and its type. We conclude with a table of properties of the methods and a summary of the simulation results, from which we identify the best methods.

Suggested Citation

  • Bauer, Frank & Lukas, Mark A., 2011. "Comparingparameter choice methods for regularization of ill-posed problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1795-1841.
    • Handle: RePEc:eee:matcom:v:81:y:2011:i:9:p:1795-1841
    DOI: 10.1016/j.matcom.2011.01.016
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    References listed on IDEAS

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    1. Cummins D. J & Filloon T. G. & Nychka D., 2001. "Confidence Intervals for Nonparametric Curve Estimates: Toward More Uniform Pointwise Coverage," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 233-246, March.
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    5. Naik, Shraddha M. & Jagannath, Ravi Prasad K. & Kuppili, Venkatanareshbabu, 2020. "Fractional Tikhonov regularization to improve the performance of extreme learning machines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

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