A robust method based on LOVO functions for solving least squares problems

The robust adjustment of nonlinear models to data is considered in this paper. When data comes from real experiments, it is possible that measurement errors cause the appearance of discrepant values, which should be ignored when adjusting models to them. This work presents a Lower Order-value Optimi...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Castelani, E V, Lopes, R, Shirabayashi, W V I, Sobral, F N C
Format: Article
Language:English
Subjects:
Algorithms
Data analysis
Economic models
Optimization
Outliers (statistics)
Robustness (mathematics)
Statistical models
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The robust adjustment of nonlinear models to data is considered in this paper. When data comes from real experiments, it is possible that measurement errors cause the appearance of discrepant values, which should be ignored when adjusting models to them. This work presents a Lower Order-value Optimization (LOVO) version of the Levenberg-Marquardt algorithm, which is well suited to deal with outliers in fitting problems. A general algorithm is presented and convergence to stationary points is demonstrated. Numerical results show that the algorithm is successfully able to detect and ignore outliers without too many specific parameters. Parallel and distributed executions of the algorithm are also possible, allowing for the use of larger datasets. Comparison against publicly available robust algorithms shows that the present approach is able to find better adjustments in well known statistical models.
ISSN:2331-8422