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LIMBARE: An Advanced Linear Mixed-Effects Breakpoint Analysis With Robust Estimation Method With Applications to Longitudinal Ophthalmic Studies. [PDF]
Transl Vis Sci TechnolLee T +5 more
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Robust kernels for robust location estimation
Neurocomputing, 2021Abstract This paper shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) F corresponds to different M-estimators in the original space depending on the kernel function associated with F . In particular, we present a proof of the correspondence of mean estimation in an RKHS for the Gaussian ...
Joseph Alejandro Gallego +2 more
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International Conference on Acoustics, Speech, and Signal Processing, 2003
A robust estimation method for frequencies of received signals is investigated. The received signals are represented as the sum of sinusoidal signals and an additive noise process. The additive noise is assumed to be a mixture of a Gaussian and an outlier process.
Sang-Geun Oh, R. L. Kashyap
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A robust estimation method for frequencies of received signals is investigated. The received signals are represented as the sum of sinusoidal signals and an additive noise process. The additive noise is assumed to be a mixture of a Gaussian and an outlier process.
Sang-Geun Oh, R. L. Kashyap
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Journal of Economic Theory, 2017
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On the robustness of batching estimators
Operations Research Letters, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yingchieh Yeh, Bruce W. Schmeiser
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Robustness and the robust estimate
Journal of Geodesy, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Econometric Reviews, 1990
This paper provides a summary of the influence function approach to robust estimation of parametric models. Hampel's optimality results for M-estimators with a bounded influence function is generalized to allow for arbitrary choices of the asymptotic efficiency criterion and the norm of the influence function.
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This paper provides a summary of the influence function approach to robust estimation of parametric models. Hampel's optimality results for M-estimators with a bounded influence function is generalized to allow for arbitrary choices of the asymptotic efficiency criterion and the norm of the influence function.
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Robust Estimation Through Estimating Equations
Biometrika, 1984The paper deals with the choice of parameter definition. It develops the concepts of parameter defining function and effective parameter. It also provides theory and techniques for choosing from a given set of robust parameters the one that can most efficiently be estimated. This theory is applied to location parameters.
Godambe, V. P., Thompson, M. E.
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Robustness in parameter estimation
IEEE Transactions on Information Theory, 1977A constructive approach to robust parameter estimation that carries over naturally to the nonparametric estimation is presented. Vagueness in previous notions of "robustness" has prevented such a connection from being made. To eliminate vagueness, robustness is defined in a precise mathematical way that leads to isolation of constructive analytical ...
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IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999
Standard least-squares (LS) methods for pose estimation of objects are sensitive to outliers which can occur due to mismatches. Even a single mismatch can severely distort the estimated pose. This paper describes a least-median of squares (LMedS) approach to estimating pose using point matches.
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Standard least-squares (LS) methods for pose estimation of objects are sensitive to outliers which can occur due to mismatches. Even a single mismatch can severely distort the estimated pose. This paper describes a least-median of squares (LMedS) approach to estimating pose using point matches.
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