robust estimator
A Robust Estimator of R = P(X > Y ) of Heavy tailed Distributions and its Sampling Distributions
... a robust estimator, namely the harmonic moment ...Hill estimator is ...of estimator of R which will help us to study the properties of the ...
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Optimal Robust Estimator-Correlator for Spectrum Sensing in Cognitive Radio
... optimal estimator-correlator (EC) detector for non- coherent spectrum sensing scenarios with isotropic signal and noise covariance ...propose robust detection schemes in order to reduce the effect of the ...
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Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization
... Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non- line-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc ...
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Data Fusion Using Robust Estimator for Uncertain Noisy Systems Over Sensor Networks
... robust Kalman filtering, the simple structure of the proposed filter makes the filter easy to design online applications. Then, we have represented, it is mostly suitable to multi-sensory applications with ...
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Robust Covariance Estimator for Small-Sample Adjustment in the Generalized Estimating Equations: A Simulation Study
... The robust or sandwich estimator is common to estimate the covariance matrix of the estimated regression parameter for generalized estimating equation (GEE) method to analyze longitudinal ...the ...
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Variance-based Regularization with Convex Objectives
... new estimator, it is essential to understand the limits of the pro- posed procedure and identify situations in which its performance may be worse than ex- isting ...the robust-regularized risk (4) yields ...
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The EWMA control chart based on robust scale estimators
... As shown in the literature that the limits based on robust estimator should outperform for non-normal process. For this purpose the second simulation is being carried out to compare the performance of EWMA ...
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Kernel filtering of spot volatility in presence of Lévy jumps and market microstructure noise
... jump-robust estimator. They show the consistency of their estimator, but are unable to establish the asymptotic normality of the estimator in the presence of ...
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Improving Efficiency and Robustness of Doubly Robust Estimators in the Presence of Coarsened Data
... “doubly robust” in that they are consistent for the true population mean even if one of the outcome regres- sion or propensity score models (but not both) is ...doubly robust estimator can be ...
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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 ...biased- robust estimator that uses a multistage Generalized M estimator with fully ...
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A robust information source estimator with sparse observations
... path-based estimator is a robust estimator and can be used in the case when the parameters of the SIR model are unknown, which is a very desirable property since knowing these parameters can be ...
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Case Mix Planning using The Technique for Order of Preference by Similarity to Ideal Solution and Robust Estimation: a Case Study
... With the view of the above-reviewed literature the aim of this study is to solve a case mix planning problem based on the actual data collected from Shahid Madani hospital. The significance of this research is that the ...
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The TSR-MM Based on Robust Location and Scales Measures in Dual Response Optimization in the Presence of Outliers and Heteroscedastic Errors
... in robust design studies, robust location (median) and robust scales estimates (Median Absolute Deviation (MAD) and Interquartile Range (IQR)) of the response variables are employed for dual response ...
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The performance of clustering approach with robust mm-estimator for multiple outlier detection in linear regression
... with robust estimator that is MM-Estimator for multiple outlier detection in linear ...LS-based estimator. Since the MM-based estimator is found to be the best estimator and more ...
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ShookSa_unc_0153D_19185.pdf
... Count outcomes are of common interest in public health research. To estimate the causal effect of a binary exposure on a count outcome with observational data, methods are needed to control for confounding variables. ...
119
Sliding Mode Control for Flexible Joint using Uncertainty and Disturbance Estimation
... is demonstrated through simulation. The control strategy includes Ackerman’s method, which eliminates reaching phase to robustify the system. UDE is used to estimate the uncertainties and disturbance. The model is ...
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Development of a robust hybrid estimator using partial least squares regression and artificial neural networks.
... The aim of this paper is ro develop a robust inferentjal estimator by usjng hybrid PLS-ANN model based on on-line measuements of process variables, such as flow raies and temperatur[r] ...
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Robust Sample Survey Inference via Bootstrapping and Bias Correction: The Case of the Ratio Estimator
... Such an alternative approach is provided by bootstrap simulation. To motivate this approach, we observe that the primary reason for constructing a confidence interval around a point estimate of Y is to provide a properly ...
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Testing for exogeneity in cointegrated panels
... OLS estimator also has a tendency of understating the con…dence interval, but this is less pronounced and it (slowly) vanishes as N and T ...FM-OLS estimator: long-run variances are di¢ cult to estimate, ...
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Improvement Over General And Wider Class Of Estimators Using Ranked Set Sampling
... Abstract: In this paper, Improvement over general and wider class of estimators of finite population means using ranked set sampling is investigated. Ranked set sampling (RSS) was first suggested to increase the ...
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