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April 2000 Empirical geometry of multivariate data: a deconvolution approach
V. I. Koltchinskii
Ann. Statist. 28(2): 591-629 (April 2000). DOI: 10.1214/aos/1016218232

Abstract

Let $\{Y_j : j = 1,\ldots,n\}$ be independent observations in $\mathbb{R}^m, m \geq 1$ with common distribution $Q$. Suppose that $Y_j = X_j + \xi_j, j = 1,\ldots,n$, where $\{X_j, \xi_j, j = 1,\ldots,n\}$ are independent, $X_ j, j = 1,\ldots,n$ have common distribution $P$ and $\xi_ j, j = 1, \ldots,n$ have common distribution $\mu$, so that $Q = P * \mu$. The problem is to recover hidden geometric structure of the support of $P$ based on the independent observations $Y_j$. Assuming that the distribution of the errors $\mu$ is known, deconvolution statistical estimates of the fractal dimension and the hierarchical cluster tree of the support that converge with exponential rates are suggested. Moreover, the exponential rates of convergence hold for adaptive versions of the estimates even in the case of normal noise $\xi_ j$ with unknown covariance. In the case of the dimension estimation, though, the exponential rate holds only when the set of all possible values of the dimension is finite (e.g., when the dimension is known to be integer). If this set is infinite, the optimal convergence rate of the estimator becomes very slow (typically, logarithmic), even when there is no noise in the observations.

Citation

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V. I. Koltchinskii. "Empirical geometry of multivariate data: a deconvolution approach." Ann. Statist. 28 (2) 591 - 629, April 2000. https://doi.org/10.1214/aos/1016218232

Information

Published: April 2000
First available in Project Euclid: 15 March 2002

zbMATH: 1105.62345
MathSciNet: MR1790011
Digital Object Identifier: 10.1214/aos/1016218232

Subjects:
Primary: 62G07, 62H12, 62H30
Secondary: 62F17

Rights: Copyright © 2000 Institute of Mathematical Statistics

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Vol.28 • No. 2 • April 2000
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