Robust approaches for monitoring logistic regression profiles under outliers

Hakimi, Ahmad; Amiri, Amirhossein; Kamranrad, Reza

doi:10.1108/ijqrm-04-2015-0053

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Among those five methods, a

T^{2}

‐based control chart in which the covariance matrix is estimated based on Equation () was shown to have the most appropriate performance. Koosha and Amiri, 13 Shadman et al, 11 and Hakimi et al 50 choose the most efficient approach proposed by Yeh et al 9 to monitor other GLM profiles including Poisson profiles. Four proposed robust estimators for logistic regression were used by Hakimi et al 50 to estimate the parameters of logistic profiles when data include outliers.…”

Section: Monitoring Glm Profiles Based On T2control Chartsmentioning

confidence: 99%

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GLM profile monitoring using robust estimators

Moheghi

Noorossana

Ahmadi

2020

Quality & Reliability Eng

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It is well known that performance of control scheme in phase II of statistical process control depends on the estimators utilized in phase I. Sometimes, outliers may be present in the data, which could seriously impact the performance of the estimators. In some practical situations, generalized linear models (GLMs) are used to model a wide class of response variables. This study deals with the robust estimation and monitoring of parameters in GLM profiles in the presence of outliers. In this study, robust estimators are used to estimate the parameters of logistic and Poisson profiles. The results are compared with the maximum likelihood estimators (MLEs). In a numerical example, the profile parameters are estimated by the MLE and robust estimators and the resulting test statistics are monitored by a control scheme. The phase II control charts are determined based on these two types of estimators and compared for different out‐of‐control conditions. The simulation results confirm that robust estimators in most cases lead to better estimates in comparison with the MLE estimator in terms of average run length criterion.

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“…Among those five methods, a

T^{2}

Section: Monitoring Glm Profiles Based On T2control Chartsmentioning

confidence: 99%

“…In this regard, Ahmadi et al 49 provided a clustering‐based robust estimator of the control profile and a change point estimator of wavelet profiles in phase II. To the best of our knowledge, the study by Hakimi et al 50 is the only study that applies robust estimation for a logistic profile that is a GLM‐based profile.…”

Section: Introductionmentioning

confidence: 99%

GLM profile monitoring using robust estimators

Moheghi

Noorossana

Ahmadi

2020

Quality & Reliability Eng

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“…Kamranrad and Amiri 20 proposed an integrated control chart based on ordinary and robust Holt‐Winter models for monitoring autocorrelated simple linear profiles in Phase II. Hakimi et al 21 . proposed several robust approaches to reduce the impact of outliers on estimating the logistic regression parameters.…”

Section: Introductionmentioning

confidence: 99%

Monitoring logistic profiles in phase I using robust cluster‐based method

Saremian

Noorossana

Raissi

et al. 2022

Quality & Reliability Eng

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In order to monitor some qualitative characteristics, it is necessary to examine the relationship between a binary response variable and one or more descriptive variables. This relationship is referred to as a logistic profile in the literature of statistical process control. Maximum likelihood estimation (MLE) is a commonly used method to estimate the parameters of logistic regression profiles. If there are outliers in a data set, this estimation method will not perform well in estimating parameters accurately. In this paper, we propose a robust cluster‐based method for monitoring logistic regression profiles in Phase I. We evaluate the performance of the proposed method versus the non‐cluster‐based methods using numerical simulation. The results show that the cluster‐based method improves the performance of control charts for monitoring logistic regression profiles and works well in detecting step shifts and outliers. This method is entirely superior to the non‐cluster‐based methods in estimating the parameters in the presence of outliers; and for medium to large shifts, it shows acceptable performance compared to the robust methods.

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“…Izadbakhsh et al (2018) applied Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in phase I. Hakimi et al (2017) proposed some robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T 2 control chart. Abbas et al (2019) developed a Bayesian way using CUSUM control charts to monitor linear profiles.…”

Section: Introductionmentioning

confidence: 99%

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Nie

Liu

et al. 2020

IJQRM

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PurposeThe purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.Design/methodology/approachThe proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.FindingsThe simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.Practical implicationsThe research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.Originality/valueDue to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

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Robust approaches for monitoring logistic regression profiles under outliers

Cited by 13 publications

References 26 publications

GLM profile monitoring using robust estimators

GLM profile monitoring using robust estimators

Monitoring logistic profiles in phase I using robust cluster‐based method

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

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