@inproceedings{4cd85612808347a2a4fcb7ddb2014de1,
title = "Evaluation of calibration results using the least median of squares method in the case of linear multivariate models",
abstract = "Robust estimation procedures make it possible to reduce the leverage of outliers on the measurement results. Application of the least median of squares (LMS) method to determine the calibration curves of measuring instruments involves the need to evaluate the obtained results. This paper proposes the method of estimating the variance of the LMS regression coefficients, which allows the evaluation of the quality of the set regression coefficients. The concept of an a priori estimation consists in performing an estimation before the measurement experiment. The a posteriori estimation uses the data from the previously conducted measurement experiment, thus making it possible to obtain much more accurate results compared with the a priori method. Simulation tests were carried out in order to compare the estimation results with the ones obtained in the course of the simulated experiment. The obtained results confirm that the proposed concepts are correct.",
keywords = "calibration, least median of squares method, robust estimators",
author = "Dariusz Buchczik and Witold Ilewicz",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016 ; Conference date: 29-08-2016 Through 01-09-2016",
year = "2016",
month = sep,
day = "22",
doi = "10.1109/MMAR.2016.7575239",
language = "English",
series = "2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "800--805",
booktitle = "2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016",
address = "United States",
}