Robust Estimators
Breakdown plot of symmetry distance based robust estimators
... Regression analysis is a vital statistical tool usually employed in computer vision for a huge variety of tasks. The least squares method is the traditional and commonly used method of computation in regression analysis. ...
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Detection of Outliers in Growth Curve Models: Using Robust Estimators
... Even though studies on the determination of points outlying of the bulk began long ago, it is only after 1990 that it has started to improve [10-14]. The purpose of this paper is to identify outliers in GCMs by using ...
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Evaluation of Robust Estimators Applied to Fluorescence Assays
... The goal of this study was to improve the accuracy and the re- peatability of the new TPX assay technology-based measure- ment by decreasing the influence of the outliers in the esti- mation of the true signal value from ...
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The use of robust estimators as measures of core inflation. NBB Working Paper Nr. 2
... of robust estimators such as the median or trimmed means, in order to downweight outliers in the cross-section of price changes in a more flexible ...
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Improving Efficiency and Robustness of Doubly Robust Estimators in the Presence of Coarsened Data
... weighted” estimators that involve modeling both the outcome regression and propensity score, with the efficient member of the class obtained when both models are ...that estimators in this class are “doubly ...
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A COMPARATIVE STUDY OF SOME ROBUST ESTIMATORS
... these Robust estimators and Ordinary Least Square were investigated and compared using the Mean Square Error (MSE) ...the estimators considered. But when outlier(s) exists in the data, Robust ...
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Doubly-robust Estimators in Observational Studies with and without a Stratified Sub-sample.
... 1.2. Estimators that take account of confounding have been proposed by, ...these estimators lead to valid inference if these models are correctly specified under the assumption that all potential ...
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A comparison of robust estimators in simple linear regression
... The bias and mean squared error (MSE) of the various estimators of slope, intercept, and mean response were estimated and compared in a simulation study.. All computing was done in Fortr[r] ...
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A COMPARATIVE STUDY OF SOME ROBUST RIDGE AND LIU ESTIMATORS
... proposed robust M-estimator for ridge regression to handle the problem of multicollinearity and ...M estimators instead of M ...biased- robust estimator that uses a multistage Generalized M estimator ...
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diakonikolas.pdf
... Exploit piecewise polynomial approximation for structured model estimation.. Algorithmic Framework for Distribution Estimation: Leads to fast & robust estimators?[r] ...
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The EWMA control chart based on robust scale estimators
... the robust approaches are ...different robust scale estimators available in literature and also compared the performance of these charts by calculating expected out-of-control points and expected ...
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SOME ROBUST RIDGE ESTIMATORS: A COMPARATIVE STUDY.
... In the presence of multicollinearity and outliers, the Ordinary Least Square estimator is found to be inefficient due to the inflated standard errors. In this paper, some forms of Generalized Ridge regression parameters ...
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Robust M-Estimators of the Population Mean.
... of the n observations in the sample, the value of the derivative is found to approach zero, the point c is a said to be an unbiased estimate of the mean of the distribution where the sample was obtained from. The main ...
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New Robust MEWMA Control Chart for Monitoring Contaminated Data
... other robust estimators such as minimum vector variance, minimum covariance determinant or modified one-step M-estimator, which is believed to be more capable in controlling the false alarm rates especially ...
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Robust estimation with the weighted trimmed likelihood estimator
... the robust weighted trimmed likelihood estimator (WTLE) to the standard GARCH ...additional robust parameter can be automatically ...introduced robust GARCH estimators and show through the ...
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Small area estimation with state space models subject to benchmark constraints
... benchmarked estimators (see below) is to include the sampling errors as part of the error terms in the observation (measurement) equation, instead of the current practice of fitting a linear time series model for ...
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Robust Inference for Time Varying Coefficient Models with Longitudinal Data
... the estimators of the coefficient functions along with the widths of the enve- lopes formed by pointwise ...the estimators among simulations, where β 0 ( ) t is ...
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Correcting data for measurement error in generalized linear models
... As all of the bias-adjusted estimators are modified M-estimators or what might be called pseudo M-estimators, a routine though tedious application of the delta method yields standard lar[r] ...
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CRL 94 1 pdf
... The spline-based motion estimation algorithms introduced in this paper are a hybrid of local optic flow algorithms and global motion estimators, utilizing the best features of both approaches. Like other local ...
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Robust and Computationally Efficient Signal-Dependent Method for Joint DOA and Frequency Estimation
... frequency estimators, the proposed method has two major advantages: (1) it provides a robust performance in the presence of colored noise; (2) it does not involve the estimation of covariance matrix and its ...
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