Title: A Family of Adaptive Robust Estimates Based on the Symmetrical Generalized Logistic Distribution
Authors: Li ×
De Moor, Bart #
Issue Date: 1999
Publisher: Marcel Dekker
Series Title: Communications in Statistics: Theory and Methods vol:28 issue:6 pages:1293-1310
Article number: 0
Abstract: A family of adaptive robust estimates which range from L-1 to L-2 estimates is suggested based on the symmetrical generalized logistic (SGL) distribution. Depending on whether existing outliers and how serious for the outliers in sampling data, this adaptive SGL estimate family can automatically choose an L-2 estimate, L-1 estimate, or smoothed Huber estimate to fit the data. It is shown that the asymptotic efficiencies of the adaptive SGL estimates relative to L-2/L-1 estimates are 1 at the normal/Laplace distribution situations respectively. Practical examples show that the SGL estimates have satisfactory flexibility to deal with different patterns of outliers in data. Hence, the adaptive SGL robust estimates are useful for automatic analysis in systems identification and are very convenient for practitioners.
Description: \emph{Communications in Statistics, Theory and Methods}, vol. 28,no. 6, 1999
ISSN: 0361-0926
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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