Results 121 to 130 of about 95,239 (168)
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Statistica Neerlandica, 1981
Summary In this paper we show that HUBER‐estimates and more general M‐estimates are bounded by the smallest and the largest trimmed mean of a sample.
Jewett, R. I., Ronner, A. E.
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Summary In this paper we show that HUBER‐estimates and more general M‐estimates are bounded by the smallest and the largest trimmed mean of a sample.
Jewett, R. I., Ronner, A. E.
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M-estimation of wavelet variance
Annals of the Institute of Statistical Mathematics, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mondal, Debashis, Percival, Donald B.
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Moderate deviations for M-estimators
Test, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Continuous M-Estimators and Their Interpolation by Polynomials
SIAM Journal on Numerical Analysis, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jong-Shi Pang, Thomas P. Y. Yu
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A remark on approximate M-estimators
Statistics & Probability Letters, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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M-Estimates of Autoregression with Random Coefficients
Automation and Remote Control, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. V. Goryainov, V. B. Goryainov
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Sensitivity analysis of M-estimates
Annals of the Institute of Statistical Mathematics, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A Berry–Esséen Bound for M‐estimators
Scandinavian Journal of Statistics, 1997We prove a Berry–Esséen bound for general M‐estimators under optimal regularity conditions on the score function and the underlying distribution. As an application we obtain Berry–Esséen bounds for the sample median, the Lp‐median, p > 1 and Huber's estimator of ...
Bentkus, V +2 more
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Canadian Journal of Statistics, 2003
AbstractThe author shows how to find M‐estimators of location whose generating function is monotone and which are optimal or close to optimal. It is easy to identify a consistent sequence of estimators in this class. In addition, it contains simple and efficient approximations in cases where the likelihood function is difficult to obtain.
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AbstractThe author shows how to find M‐estimators of location whose generating function is monotone and which are optimal or close to optimal. It is easy to identify a consistent sequence of estimators in this class. In addition, it contains simple and efficient approximations in cases where the likelihood function is difficult to obtain.
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Robust M-estimates and generalized M-estimates for autoregressive parameter estimation
Fourth IEEE Region 10 International Conference TENCON, 2003The 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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