Sentence examples for robust estimation approaches from inspiring English sources

LTS regression produced slopes that were often comparable to those associated with the other robust estimation approaches [see Supplemental Material, Tables S8 S12 (http://dx.doi.org/10.1289/ehp.1205793)], with equivalent slopes produced by the four methods for 50% of the simulations and slopes within 20% of each other for 63% of the simulations.

One common robust estimation approach is the M-estimator, which is based on a maximum likelihood estimation (MLE).

A detailed description of this robust estimation approach for obtaining optimal parameter estimates from species concentration dynamics during muscle ischemia and recovery is presented in Ref. [1].

In particular, this work proposes a robust estimation approach to the problem of the design of residual generators in order to realise complete diagnosis schemes when realistic faults are present on the simulated system.

In order to account for non-independence among relatives, a robust variance estimation approach was used [ 20].

Moreover, as it will be shown later, the estimated parameters seem to be in line with economic intuition and are robust to different estimation approaches.

Although our approach is based on a maximum likelihood estimate, robust estimation of multiple emitter positions also requires strategies such as making good initial estimates, making accurate uncertainty estimates and the model selection and rejection algorithm.

To implement this approach, a robust estimation of the overall genetic diversity throughout the genome first had to be obtained.

Simultaneous approaches consisting of robust estimation method (REM) and wavelet transform (WT) were proposed to reduce outliers and noises of the input data for the SVR model.

Hence, there is need for a posteriori error estimation approaches which are robust for coarse meshes and which are constructed using non-intrusive approaches with respect to the employed software.

The first is the development of specialized analysis procedures that are resistant to the anomalies in the data, extending standard analysis methods using fundamental ideas from robust statistics, such as the robust time-series modeling approach described by Martin and Yohai [10] or the robust-resistant spectrum estimation approach described by Martin and Thomson [11].