Abstract
Recovery of the unknown parameter in an abstract inverse estimation model can be based on regularizing the inverse of the operator defining the model. Such regularized-inverse type estimators are constructed with the help of a version of the spectral theorem due to Halmos, after suitable preconditioning. A lower bound to the minimax risk is obtained exploiting the van Trees inequality. The proposed estimators are shown to be asymptotically optimal in the sense that their risk converges to zero, as the sample size tends to infinity, at the same rate as this lower bound. The general theory is applied to deconvolution on locally compact Abelian groups, including both indirect density and indirect regression function estimation.
Similar content being viewed by others
References
Bowman, A. W. (1984). An alternative method of cross-validation for the smoothing of density estimates, Biometrika, 71, 353-360.
Brown, L. D. and Low, M. G. (1996). Asymptotic equivalence of nonparametric regression and white noise, Ann. Statist., 24, 2384-2398.
Carroll, R. J. and Hall, P. (1988). Optimal rates of convergence for deconvolving a density, J. Amer. Statist. Assoc., 83, 1184-1186.
Carroll, R. J., van Rooij, A. C. M. and Ruymgaart, F. H. (1991). Theoretical aspects of ill-posed problems in statistics, Acta Appl. Math., 24, 113-140.
Chandrawansa, K., van Rooij, A. C. M. and Ruymgaart, F. H. (1999). Speed of convergence in the Hausdorff metric for estimators of irregular mixing densities, J. Nonparametr. Statist., 10, 375-387.
Chauveau, D., van Rooij, A. C. M. and Ruymgaart, F. H. (1994). Regularized inversion of noisy Laplace transforms, Adv. in Appl. Math., 15, 186-201.
Dey, A. K., Mair, B. A. and Ruymgaart, F. H. (1996). Cross-validation for parameter selection in inverse estimation problems, Scand. J. Statist., 23, 609-620.
Dey, A. K., Martin, C. F., and Ruymgaart, F. H. (1998). Input recovery from noisy output data, using regularized inversion of the Laplace transform, IEEE Trans. Inform. Theory, 44, 1125-1130.
Diaconis, P. (1988). Group Representations in Probability and Statistics, Lecture Notes-Monograph Series #11, Institute of Mathematical Statistics, Hayward, California.
Donoho, D. L. (1994). Statistical estimation and optimal recovery, Ann. Statist., 22, 238-270.
Donoho, D. L. (1995). Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition, Appl. Comput. Harmon. Anal., 2, 101-126.
Efromovich, S. and Pinsker, M. S. (1984). An adaptive algorithm of nonparametric filtering, Automat. Remote Control, 11, 1434-1440.
Fan, J. (1991). Global behavior of deconvolution kernel estimates, Statist. Sinica, 1, 541-551.
Gill, R. D. and Levit, B. Y. (1995). Applications of the van Trees inequality: A Bayesian Cramér-Rao bound, Bernoulli, 1, 59-79.
Gilliam, D. S., Schulenberger, J. R. and Lund, J. H. (1988). Spectral representation of the Laplace and Stieltjes transforms, Mat. Apl. Comput., 7, 101-107.
Goldenshluger, A. (1997). On pointwise adaptive nonparametric deconvolution, Tech. Report, University of Haifa, Haifa, Israel.
Golubev, G. K. and Nussbaum, M. (1990). A risk bound in Sobolev class regression, Ann. Statist., 18, 758-778.
Groeneboom, P. and Jongbloed, G. (1995). Isotonic estimation and rates of convergence in Wicksell's problem, Ann. Statist., 23, 1518-1542.
Hájek, J. and Šidák, Z. (1967). Theory of Rank Tests, Academic Press, New York.
Hall, P. (1990). Optimal convergence rates in signal recovery, Ann. Probab., 18, 887-900.
Halmos, P. R. (1963). What does the spectral theorem say? Amer. Math. Monthly, 70, 241-247.
Healy, D. M. Jr. and Kim, P. T. (1993). Spherical deconvolution with application to geometric quality assurance, Tech. Report, Dept. Mathematics and Computer Science, Dartmouth College.
Healy, D. M. Jr. and Kim, P. T. (1996). An empirical Bayes approach to directional data and efficient computation on the sphere, Ann. Statist., 24, 232-254.
Healy, D. M. Jr., Hendriks, H. and Kim, P. T. (1998). Spherical deconvolution, J. Multivariate Anal., 67, 1-22.
Hewitt, E. and Ross, K.A. (1963). Abstract Harmonic Analysis, I, Springer, New York.
Hoerl, A. and Kennard, R. W. (1970a). Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, 55-67.
Hoerl, A. and Kennard, R. W. (1970b). Ridge regression: Application to nonorthogonal problems, Technometrics, 12, 69-82.
Johnstone, I. M. and Silverman, B. W. (1990). Speed of estimation in positron emission tomography and related inverse problems, Ann. Statist., 18, 251-280.
Kim, P. T. (1998). Deconvolution density estimation on SO (N), Ann. Statist., 26, 1083-1102.
Koo, J.-Y. (1993). Optimal rates of convergence for nonparametric statistical inverse problems, Ann. Statist., 21, 590-599.
Kress, R. (1989). Linear Integral Equations, Springer, New York.
Mair, B. A. and Ruymgaart, F. H. (1995). Statistical inverse estimation in Hilbert scales, SIAM J. Appl. Math., 56, 1424-1444.
Mardia, K. V. and Jupp, P. (2000). Directional Statistics, Wiley, New York.
Margenau, H. and Murphy, G. M. (1956). The Mathematics of Physics and Chemistry, 2nd ed., Van Nostrand, Princeton, New Jersey.
Marron, J. S. and Tsybakov, A. B. (1995). Visual error criteria for qualitative smoothing, J. Amer. Statist. Assoc., 90, 499-507.
Neumann, M. H. (1995). Optimal change-point estimation in inverse problems, Preprint #163, Weierstrass-Institut, Berlin.
Nussbaum, M. (1996). The Pinsker bound: A review, Preprint #281, Weierstrass Institut, Berlin.
Nychka, D. and Cox, D. D. (1989). Convergence rates for regularized solutions of integral equations from discrete noisy data, Ann. Statist., 17, 556-572.
Pinsker, M. S. (1980). Optimal filtering of square integrable signals in Gaussian white noise, Problems Inform. Transmission, 16, 120-133.
Rudemo, M. (1982). Empirical choice of histograms and kernel density estimators, Scand. J. Statist., 9, 65-78.
Terras, A. (1985). Harmonic Analysis on Symmetric Spaces and Applications, I, Springer, New York.
Tikhonov, A. N. and Arsenin, V. Y. (1977). Solutions of Ill-posed Problems, Wiley, New York.
van Rooij, A. C. M. and Ruymgaart, F. H. (1991). Regularized deconvolution on the circle and the sphere, Nonparametric Functional Estimation and Related Topics (ed. G. G. Roussas), Kluwer, Dordrecht.
van Rooij, A. C. M. and Ruymgaart, F. H. (1996). Asymptotic minimax rates for abstract linear estimators, J. Statist. Plann. Inference, 53, 389-402.
Vapnik, V. (1982). Estimation of Dependences Based on Empirical Data, Springer, New York.
Zhang, C.-H. (1990). Fourier methods for estimating mixing densities and distributions, Ann. Statist., 18, 806-831.
Author information
Authors and Affiliations
About this article
Cite this article
van Rooij, A.C., Ruymgaart, F.H. Abstract Inverse Estimation with Application to Deconvolution on Locally Compact Abelian Groups. Annals of the Institute of Statistical Mathematics 53, 781–798 (2001). https://doi.org/10.1023/A:1014665305349
Issue Date:
DOI: https://doi.org/10.1023/A:1014665305349