Results 221 to 230 of about 865,609 (245)

Robust M-estimates and generalized M-estimates for autoregressive parameter estimation

Fourth IEEE Region 10 International Conference TENCON, 2003
The problem of robust estimation of autoregressive parameters in the presence of outliers is considered. The least squares estimate lacks efficiency robustness when innovation outliers are present. Several M-estimates (maximum likelihood type) corresponding to different cost functions show good efficiency robustness against innovation outliers.
A. Basu, K.K. Paliwal
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Moderate deviations for M-estimators

Test, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Self-tuning M-estimators

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.
G. Agamennoni, P. Furgale, R. Siegwart
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Cointegration analysis using M estimators

Economics Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sensitivity analysis of M-estimates

Annals of the Institute of Statistical Mathematics, 1996
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M-Estimation for dependent random variables

Statistics & Probability Letters, 2002
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M-Estimation in Cross-Over Trials

Biometrics, 1994
A robust procedure, combined M-estimation, is proposed for analyzing cross-over data with possible within- and between-subject outliers. The mean squared error properties of these combined M-estimates for direct treatment effect contrasts and carryover treatment effect contrasts are examined through simulation studies.
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Efficient M-estimators with auxiliary information [PDF]

Journal of Statistical Planning and Inference, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Type M estimators

1983
The type M estimators, also called M estimators, are generalizations of the usual maximum likelihood estimates. ϑ is classically the parameter value maximizing the likelihood function, i. e. we have in obvious notation $$ L = \Pi f({x_i}|\vartheta ) = \max {\rm{for }}\vartheta $$ or equivalently $$ - \ln {\rm{ }}L{\rm{ = - }}\sum {\rm{ ln ...
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Isotonic M-Estimation

1986
Robust partitioning algorithms for isotonic regression are shown to have anomalous behavior.
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