Skip to main content
Log in

General regularization schemes for signal detection in inverse problems

  • Published:
Mathematical Methods of Statistics Aims and scope Submit manuscript

Abstract

The authors discuss how general regularization schemes, in particular, linear regularization schemes and projection schemes, can be used to design tests for signal detection in statistical inverse problems. It is shown that such tests can attain the minimax separation rates when the regularization parameter is chosen appropriately. It is also shown how to modify these tests in order to obtain a test which adapts (up to a log log factor) to the unknown smoothness in the alternative. Moreover, the authors discuss how the so-called direct and indirect tests are related in terms of interpolation properties.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ya. Baraud, “Non-Asymptotic Minimax Rates of Testing in Signal Detection”, Bernoulli 8(5), 577–606 (2002).

    MathSciNet  MATH  Google Scholar 

  2. Ya. Baraud, S. Huet, and B. Laurent, “Adaptive Tests of Linear Hypotheses by Model Selection”, Ann. Statist. 31, 225–251 (2003).

    Article  MathSciNet  MATH  Google Scholar 

  3. N. Bissantz, Th. Hohage, A. Munk, and F. H. Ruymgaart, “Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications”, SIAM J. Numer. Anal. 45(6), 2610–2636 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  4. G. Blanchard and P. Mathé, “Discrepancy Principle for Statistical Inverse Problems with Application to Conjugate Gradient Regularization”, InverseProblems 28(11), 115011 (2012).

    Google Scholar 

  5. R. Bojanic and E. Seneta, “AUnified Theory of Regularly Varying Sequences”, Math. Z. 134, 91–106 (1973).

    Article  MathSciNet  MATH  Google Scholar 

  6. A. Caponnetto, Optimal Rates for Regularization Operators in Learning Theory, Techn. Rep. CSAILTR 2006-062 (Massachusetts Inst. of Technology, 2006).

    Google Scholar 

  7. L. Cavalier, “Inverse Problems in Statistics”, in Lect. Notes Statist. Proc., Vol. 203: Inverse Problems and High-Dimensional Estimation (Springer, Heidelberg, 2011), pp. 3–96.

    Google Scholar 

  8. C. de Boor, “Bounding the Error in Spline Interpolation”, SIAMRev. 16, 531–544 (1974).

    Article  MATH  Google Scholar 

  9. H. W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, in Mathematics and its Applications (Kluwer Academic Publishers Group, Dordrecht, 1996), Vol. 375.

    Google Scholar 

  10. M. Fromont and B. Laurent, “Adaptive Goodness-of-Fit Tests in a Density Model”, Ann. Statist. 34, 1–45 (2006).

    Article  MathSciNet  Google Scholar 

  11. B. Hofmann and P. Mathé, “Analysis of Profile Functions for General Linear Regularization Methods”, SIAM J. Numer. Anal. 45(3), 1122–1141 (electronic) (2007).

    Article  MathSciNet  MATH  Google Scholar 

  12. Yu. I. Ingster, “Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. I”, Math. Methods Statist. 2(2), 85–114 (1993).

    MathSciNet  MATH  Google Scholar 

  13. Yu. I. Ingster, Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. II”, Math. Methods Statist. 2(3), 171–189 (1993).

    MathSciNet  MATH  Google Scholar 

  14. Yu. I. Ingster, “Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. III”, Math. Methods Statist. 2(4), 249–268 (1993).

    MathSciNet  MATH  Google Scholar 

  15. Yu. I. Ingster, T. Sapatinas, and I. A. Suslina, “Minimax Signal Detection in Ill-Posed Inverse Problems”, Ann. Statist. 40, 1524–1549 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  16. Q. Jin and P. Mathé, “Oracle Inequality for a Statistical Raus-Gfrerer-Type Rule”, SIAM/ASA J. Uncertainty Quantification 1(1), 386–407 (2013).

    Article  MATH  Google Scholar 

  17. B. Laurent, J.-M. Loubes, and C. Marteau, “Testing Inverse Problems: A Direct or an Indirect Problem?”, J. Statist. Plann. Inference 141(5), 1849–1861 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  18. B. Laurent, J.-M. Loubes, and C. Marteau, “Non-Asymptotic Minimax Rates of Testing in Signal Detection with Heterogeneous Variances”, Electron. J. Statist. 6, 91–122 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  19. M. Ledoux and M. Talagrand, Probability in Banach Spaces. Isoperimetry and Processes (Springer-Verlag, Berlin, 1991).

    Book  MATH  Google Scholar 

  20. P. Mathé and B. Hofmann, “How General are General Source Conditions?”, Inverse Problems 24(1), 015009, 5 (2008).

    Article  MathSciNet  Google Scholar 

  21. P. Mathé and N. Schöne, “Regularization by Projection in Variable Hilbert Scales”, Appl. Anal. 87(2), 201–219 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  22. P. Mathé and U. Tautenhahn, “Interpolation in Variable Hilbert Scales with Application to Inverse Problems”, Inverse Problems 22(6), 2271–2297 (2006).

    Article  MathSciNet  MATH  Google Scholar 

  23. F. Natterer, “Regularisierung schlecht gestellter Probleme durch Projektionsverfahren”, Numer. Math. 28(3), 329–341 (1977).

    Article  MathSciNet  MATH  Google Scholar 

  24. V. Spokoiny, “Adaptive Hypothesis Testing Using Wavelets”, Ann. Statist. 24, 2477–2498 (1996).

    Article  MathSciNet  MATH  Google Scholar 

  25. G.M. Vaĭnikko and U. A. Khyamarik. “Projection Methods and Self-Regularization in Ill-Posed Problems”, Izv. Vyssh. Uchebn. Zaved. Mat. 84(10), 3–17 (1985).

    Google Scholar 

  26. Tong Zhang. “Learning Bounds for Kernel Regression Using Effective Data Dimensionality”, Neural Comput. 17(9), 2077–2098 (2005).

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Marteau.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marteau, C., Mathé, P. General regularization schemes for signal detection in inverse problems. Math. Meth. Stat. 23, 176–200 (2014). https://doi.org/10.3103/S1066530714030028

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1066530714030028

Keywords

2000 Mathematics Subject Classification

Navigation