M-Estimation Method Based Asymmetric Objective Function
Statistics Theory
2017-02-02 v1 Statistics Theory
Abstract
The asymmetric objective function is proposed as an alternative to Huber objective function to model skewness and obtain robust estimators for the location, scale and skewness parameters. The robustness and asymptotic properties of the asymmetric M-estimators are explored. A simulation study and real data examples are given to illustrate the performance of proposed asymmetric M-estimation method over the symmetric M-estimation method. It is observed from the simulation results that the asymmetric M-estimators perform better than Huber M-estimators when the data have skewness. The application on regression is also considered.
Cite
@article{arxiv.1702.00378,
title = {M-Estimation Method Based Asymmetric Objective Function},
author = {Mehmet Niyazi Cankaya and Olcay Arslan},
journal= {arXiv preprint arXiv:1702.00378},
year = {2017}
}
Comments
31 pages