Results 241 to 250 of about 863,157 (269)
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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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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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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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1996
We consider a linear regression model $$y = X\beta + \varepsilon $$ where y is a response variable, X is an n×p design matrix of rank p, and ∈ is a vector with i.i.d. random variables.
Håkan Ekblom, Hans Bruun Nielsen
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We consider a linear regression model $$y = X\beta + \varepsilon $$ where y is a response variable, X is an n×p design matrix of rank p, and ∈ is a vector with i.i.d. random variables.
Håkan Ekblom, Hans Bruun Nielsen
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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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Moderate deviations for M-estimators
Test, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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.
G. Agamennoni, P. Furgale, R. Siegwart
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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.
G. Agamennoni, P. Furgale, R. Siegwart
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Cointegration analysis using M estimators
Economics Letters, 2001zbMATH 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, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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M-Estimation for dependent random variables
Statistics & Probability Letters, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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