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Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment. [PDF]
Stat MedHøjbjerre-Frandsen E +2 more
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Approximating evidence via bounded harmonic means. [PDF]
Stat ComputNaderi D, Robert CP, Kamary K, Wraith D.
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2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
We propose a sequential M-estimation algorithm as an alternative to sequential least squares. Being an approximation of the exact M-estimator, the proposed technique is robust to nonGaussian processes and outperforms sequential least squares. Simulation results demonstrate the power of the proposed sequential M-estimator.
Duc Son Pham 0001 +3 more
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We propose a sequential M-estimation algorithm as an alternative to sequential least squares. Being an approximation of the exact M-estimator, the proposed technique is robust to nonGaussian processes and outperforms sequential least squares. Simulation results demonstrate the power of the proposed sequential M-estimator.
Duc Son Pham 0001 +3 more
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Computational Statistics & Data Analysis, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH 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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2015 IEEE International Conference on Robotics and Automation (ICRA), 2015
M-estimators are the de-facto standard method of robust estimation in robotics. They are easily incorporated into iterative non-linear least-squares estimation and provide seamless and effective handling of outliers in data. However, every M-estimator's robust loss function has one or more tuning parameters that control the influence of different data.
Gabriel Agamennoni +2 more
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M-estimators are the de-facto standard method of robust estimation in robotics. They are easily incorporated into iterative non-linear least-squares estimation and provide seamless and effective handling of outliers in data. However, every M-estimator's robust loss function has one or more tuning parameters that control the influence of different data.
Gabriel Agamennoni +2 more
openaire +1 more source