Results 231 to 240 of about 368,778 (261)
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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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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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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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A robust Liu regression estimator
Communications in Statistics - Simulation and Computation, 2017The least-squares regression estimator can be very sensitive in the presence of multicollinearity and outliers in the data. We introduce a new robust estimator based on the MM estimator.
Peter Filzmoser, Fatma Sevinç Kurnaz
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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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Robust estimation for circular data
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Anipose: A toolkit for robust markerless 3D pose estimation
Cell Reports, 2021Lili Karashchuk +2 more
exaly
Robust multivariate mean estimation: The optimality of trimmed mean
Annals of Statistics, 2021, Gabor Lugosi
exaly
Robust Sets of Regression Estimates
Econometrica, 1983Gilstein, C Zachary, Leamer, Edward E
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