%0 Journal Article
%T The competition of robust methods in linear and nonlinear regression with heavy-tailed stable errors
%J AUT Journal of Mathematics and Computing
%I Amirkabir University of Technology
%Z 2783-2449
%A Bassam Shiekh Albasatneh, Mohammad
%A Naghshineh Arjmand, Omid
%D 2024
%\ 05/05/2024
%V
%N
%P -
%! The competition of robust methods in linear and nonlinear regression with heavy-tailed stable errors
%K Robust regression methods
%K Least Trimmed Squares
%K exact coeﬃcient estimators
%K heavy-tailed stable errors
%R 10.22060/ajmc.2024.22960.1207
%X Robust regression methods including Least Trimmed Squares are one of the most important methodologies to compute exact coeﬃcient estimators when data is polluted with outliers. There is interest to generalize Least Trimmed Squares for regression models with heavy-tailed stable errors. In this manuscript, we compare estimating coeﬃcients methods with the robust Least Trimmed Squares method in stable errors case. Therefore, we propose Stable Least Trimmed Squares and Nonlinear Stable Least Trimmed Squares methods for linear/nonlinear regression models with stable errors, respectively. The joint distribution of ordered errors is used with the ﬁnite variance property of ordered stable errors, whose indexes are deﬁned by cut-oﬀ points. We make many comparisons using simulated and real datasets.
%U