Robust approaches for monitoring logistic regression profiles under outliers

Abstract

Purpose

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T² control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

Design/methodology/approach

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T² control chart.

Findings

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

Practical implications

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

Originality/value

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

Citation

Hakimi, A., Amiri, A. and Kamranrad, R. (2017), "Robust approaches for monitoring logistic regression profiles under outliers", International Journal of Quality & Reliability Management, Vol. 34 No. 4, pp. 494-507. https://doi.org/10.1108/IJQRM-04-2015-0053