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- W2913982017 abstract "Robust impedance estimation procedures are now in standard use in magnetotelluric (MT) measurements and research. These always yield impedance estimates which are better than the conventional least square (LS) estimation because the 'real' MT data almost never satisfy the statistical assumptions of Gaussian distribution and stationarity upon which normal spectral analysis is based. The robust estimation procedures are commonly based on M-estimators that have the ability to reduce the influence of unusual data (outliers) in the response (electric field) variables, but are often not sensitive to exceptional predictors (magnetic field) data, which are termed leverage points.This paper proposes a procedure for making unbiased robust estimates of MT impedance functions. The means for accomplishing this is based on the bounded-influence regression M-estimation and the Hilbert Transform operating on minimum phase MT impedance functions. A bounded-influence robust estimator is here developed to simultaneously provide protection from the influence of outliers in both response and input variables. This new robust estimator combines a standard robust M-estimator with leverage weighting based on the statistics of the hat matrix, which is a standard statistical measure of unusual input. On those bases, outlier contamination is removed from the resulting regression estimates and the bias problem is solved. Using real MT data, it is shown that the method can produce improved MT impedance functions even under conditions of severe noise contamination and in the absence of remote reference data." @default.
- W2913982017 created "2019-02-21" @default.
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- W2913982017 date "2005-06-01" @default.
- W2913982017 modified "2023-09-26" @default.
- W2913982017 title "Phase-smoothed Robust Estimation of Magnetotelluric Impedance Functions Based on A Bounded-influence Regression M-estimator" @default.
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