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In our experiments, we actually use a robust estimator to estimate noise level using the following formula:, here, represents the corresponding curvelet coefficients at scale and angle, and MED represents the medium operator to calculate the medium value for a sequence coefficients.
We calculated variance with a robust estimator, to avoid the influence of outliers, as described in [ 90, 91].
Therefore, GEE models with a logit link and an exchangeable correlation matrix were fitted to produce a population-averaged odds ratio (OR) and a robust estimator to correct the standard errors for the potential correlation of observations within hospitals [ 22].
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Because the operative units are in turn nested into Local Health Authority Hospitals or Hospital Trusts, we used a cluster robust estimator to obtain a reliable estimate of the standard errors for regression coefficients [ 21].
Specifically, we have used the MM-estimator, an effective robust estimator to map the KIDSCREEN-10 to CHU9D.
The standard errors are slightly different; the standard error from the LOGISTIC procedure is less than the standard error from the SURVEYLOGISTIC procedure, because the latter uses a sandwich-type robust estimator to account for strata and the sampling proportion.
In particular, they propose a control loop implementation that is based on a model of the web service and a robust estimator, used to keep the QoS values in accordance to the SLA.
A robust estimator is designed to simultaneously estimate the state of the system and the actuator fault.
A robust estimator is supposed to provide good estimation performance over a large set of different models (in the application discussed here, the set of CES models), even if not optimal under any nominal (matched or mismatched) one.
Under mild structural conditions a robust estimator is designed to solve the problem.
As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) designed by Basu et al.
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