TY - JOUR
ID - 5404
TI - The competition of robust methods in linear and nonlinear regression with heavy-tailed stable errors
JO - AUT Journal of Mathematics and Computing
JA - AJMC
LA - en
SN - 2783-2449
AU - Bassam Shiekh Albasatneh, Mohammad
AU - Naghshineh Arjmand, Omid
AD - Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran
Y1 - 2024
PY - 2024
VL -
IS -
SP -
EP -
KW - Robust regression methods
KW - Least Trimmed Squares
KW - exact coeﬃcient estimators
KW - heavy-tailed stable errors
DO - 10.22060/ajmc.2024.22960.1207
N2 - 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.
UR - https://ajmc.aut.ac.ir/article_5404.html
L1 -
ER -