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Robust Maximum Likelihood Estimation
INFORMS Journal on Computing, 2019In many applications, statistical estimators serve to derive conclusions from data, for example, in finance, medical decision making, and clinical trials. However, the conclusions are typically dependent on uncertainties in the data. We use robust optimization principles to provide robust maximum likelihood estimators that are protected against data ...
Dimitris Bertsimas, Omid Nohadani
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IFAC Proceedings Volumes, 1990
Abstract An H ∞ approach is considered to the problem of designing a robust linear estimator for discrete time stationary systems. This represents a new class of estimation problem motivated by the need to keep the estimation error spectrum small over a range of frequencies. There follows a brief review of the previous work, a comparison between the
M.J. Grimble, D. Ho, A. Elsayed
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Abstract An H ∞ approach is considered to the problem of designing a robust linear estimator for discrete time stationary systems. This represents a new class of estimation problem motivated by the need to keep the estimation error spectrum small over a range of frequencies. There follows a brief review of the previous work, a comparison between the
M.J. Grimble, D. Ho, A. Elsayed
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Highly Robust Variogram Estimation
Mathematical Geology, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust Average Derivative Estimation [PDF]
, 2007 Many important models, such as index models widely used in limited dependent variables, partial linear models and nonparametric demand studies utilize estimation of average derivatives (sometimes weighted) of the conditional mean function. Asymptotic results in the literature focus on situations where the ADE converges at parametric rates (as a result ...
Victoria Zinde-Walsh +1 more
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Robust Estimators for Estimating Discontinuous Functions
Metrika, 2002We study the asymptotic behavior of a wide class of kernel estimators for estimating an unknown regression function. In particular we derive the asymptotic behavior at discontinuity points of the regression function. It turns out that some kernel estimators based on outlier robust estimators are consistent at jumps.
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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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Robust Estimation and Robust Parameter
2020This chapter is addressed to the problem of defining the parameter in a semiparametric situation. Suppose, for example, that the observation X is assumed to be expressed as \(X=\theta +\varepsilon \), where \(\theta \) is the parameter to be estimated and \(\varepsilon \) is the error whose distribution is not specified by a finite number of parameters.
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
2012
The problem of robust parameter estimation in image registration is discussed and various robust methods for estimating registration parameters under outliers and inaccurate correspondences are reviewed and compared. After reviewing ordinary least-squares and weighted least-squares estimation, robust estimators such as maximum likelihood (M), repeated ...
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The problem of robust parameter estimation in image registration is discussed and various robust methods for estimating registration parameters under outliers and inaccurate correspondences are reviewed and compared. After reviewing ordinary least-squares and weighted least-squares estimation, robust estimators such as maximum likelihood (M), repeated ...
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