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Proceedings ArticleDOI

Minimax robust estimation of location and minimizing fisher information

Edward Price, +1 more
- Vol. 15, Iss: 15, pp 443-444
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TLDR
In this article, the authors considered the minimax robust estimation of a location parameter as introduced by P. J. Huber, and proposed a Lagrange multiplier technique for calculating the probability distribution with minimum Fisher information over the associated distribution set.
Abstract
Asymptotic minimax robust estimation of a location parameter as introduced by P. J. Huber is considered. This problem is closely related to recent developments in robust detection theory and robust recursive filtering for linear systems. For a given minimax location estimation problem two distinct estimates which are solutions are determined by the probability distribution Fo which has minimum Fisher information over the associated distribution set. A Lagrange multiplier technique is given for calculating Fo for a broad class of distribution sets. Existence and uniqueness conditions for Fo are given.

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References
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Journal ArticleDOI

Robust Estimation of a Location Parameter

TL;DR: In this article, a new approach toward a theory of robust estimation is presented, which treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators that are asyptotically most robust (in a sense to be specified) among all translation invariant estimators.
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Robust bayesian estimation for the linear model and robustifying the Kalman filter

TL;DR: In this article, robust Bayesian estimates of the vector x are constructed for the following two distinct situations: (1) the state x is Gaussian and the observation error v is (heavy-tailed) non-Gaussian and (2) the states x and v are Gaussian.
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Robust estimation via stochastic approximation

TL;DR: The p-point estimator (PPE) as discussed by the authors is a robust estimator based on recursive Robbins-Monro-type stochastic approximation (SA) algorithm, which is shown to be asymptotically min-max robust.
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Asymptotically robust detection of a known signal in contaminated non-Gaussian noise

TL;DR: The sign detector is shown to be the asymptotically most robust detector when g(x) is a double-exponential density.
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Robust estimation of signal amplitude

TL;DR: Simulation results show that both the iterative limiter estimator (ILE) and the ILCE possess a high degree of robustness for seemingly mild, but potent, deviations from the Gaussian model.
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