Abstract
This paper is devoted to multidimensional inverse problems. In this setting, we address the goodness-of-fit testing problem. We investigate the separation rates associated with different kinds of smoothness assumptions and different degrees of ill-posedness.
Similar content being viewed by others
References
Y. Baraud, “Non-Asymptotic Minimax Rates of Testing in Signal Detection”, Bernoulli 8(5), 577–606 (2002).
Y. Baraud, S. Huet, and B. Laurent, “Adaptive Tests of Linear Hypotheses by Model Selection”, Ann. Statist. 31, 225–251 (2003).
N. Bissantz, T. Hohage, A. Munk, and F. Ruymgaart, “Convergence Rates of General Regularization Methods for Statistical Inverse Problems and Applications”, SIAM J. Numer. Anal. 45(6), 2610–2636 (2007).
C. Butucea, “Goodness-of-Fit Testing and Quadratic Functional Estimation from Indirect Observations”, Ann. Statist. 35(5), 1907–1930 (2007).
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.
L. Cavalier and N.W. Hengartner, “Adaptive Estimation for Inverse Problems with Noisy Operators”, Inverse Problems 21(4), 1345–1361 (2005).
S. Delattre, M. Hoffmann, D. Picard, and T. Vareschi, “Blockwise SVD with Error in the Operator and Application to Blind Deconvolution”, Electron. J. Statist. 6, 2274–2308 (2012).
H. W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, in Mathematics and Its Applications. (Kluwer, Dordrecht, 1996), Vol. 375.
Yu. I. Ingster, “Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. I”, Math. Methods Statist. 2(2), 85–114 (1993).
Yu. I. Ingster, “Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. II”, Math. Methods Statist. 2(3), 171–189 (1993).
Yu. I. Ingster, “Asymptotically Minimax Hypothesis Testing for Nonparametric Alternatives. III”, Math. Methods Statist. 2(4), 249–268 (1993).
Yu. I. Ingster and N. Stepanova, “Estimation and Detection of Functions from Anisotropic Sobolev Classes”, Electron. J. Statist. 5, 484–506 (2011).
Yu. I. Ingster and I. A. Suslina, “Estimation and Hypothesis Testing for Functions from Tensor Products of Spaces”, Zap. Nauchn. Sem. S.-Peterburg. Otdel. Mat. Inst. Steklov (POMI), 351 (Veroyatnost i Statistika, 12), 180–218, 301–302 (2007).
Yu. I. Ingster, T. Sapatinas, and I. A. Suslina, “Minimax Nonparametric Testing in a Problem Related to the Radon Transform”, Math. Methods Statist. 20, 347–364 (2011).
Yu. I. Ingster, T. Sapatinas, and I. A. Suslina, “Minimax Signal Detection in Ill-Posed Inverse Problems”, Ann. Statist. 40, 1524–1549 (2012).
C. Lacour and T. M. Pham Ngoc, “Goodness-of-Fit Test for Noisy Directional Data”, Bernoulli (2014) (in press).
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).
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).
C. Marteau and P. Mathé, “General Regularization Schemes for Signal Detection in Inverse Problems”, Math. Methods Statist. 23(3) (to appear).
P. Mathé and S. V. Pereverzev, “Optimal Discretization of Inverse Problems in Hilbert sScales. Regularization and Self-Regularization of Projection Methods”, SIAM J. Numer. Anal. (electronic) 38(6), 1999–2021 (2001).
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Ingster, Y., Laurent, B. & Marteau, C. Signal detection for inverse problems in a multidimensional framework. Math. Meth. Stat. 23, 279–305 (2014). https://doi.org/10.3103/S1066530714040036
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1066530714040036