Robust AFT-based monitoring procedures for reliability data

Asadzadeh, Shervin; Baghaei, Arash

doi:10.1080/16843703.2018.1522997

Cited by 14 publications

(13 citation statements)

References 22 publications

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“…So, it is also important to extend our approach to develop robust inference procedures under such regression set‐ups. However, while there have been few attempts to develop robust estimation procedures for the AFT models 37‐39 and the semiparametric Cox regression model, 40,41 the robust testing problem still remains unexplored in the literature as discussed earlier for the standard survival data models. In conformity with the more efficient parametric survival models considered in this article, Ghosh and Basu 12 considered a parametric version of the popular Cox regression model and studied the MDPDE (and also the more general M‐estimators) for robustly estimating the model parameters.…”

Section: Future Extensions: Model Selection and Parametric (Cox) Regrmentioning

confidence: 99%

Robust Wald‐type tests under random censoring

Ghosh

Basu

Pardo

2020

Statistics in Medicine

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Randomly censored survival data are frequently encountered in biomedical or reliability applications and clinical trial analyses. Testing the significance of statistical hypotheses is crucial in such analyses to get conclusive inference but the existing likelihood‐based tests, under a fully parametric model, are extremely nonrobust against outliers in the data. Although there exists a few robust estimators given randomly censored data, there is hardly any robust testing procedure available in the literature in this context. One of the major difficulties here is the construction of a suitable consistent estimator of the asymptotic variance of robust estimators, since the latter is a function of the unknown censoring distribution. In this article, we take the first step in this direction by proposing a consistent estimator of asymptotic variance of the M‐estimators based on randomly censored data without any assumption on the censoring scheme. We then describe and study a class of robust Wald‐type tests for parametric statistical hypothesis, both simple as well as composite, under such a set‐up. Robust tests for comparing two independent randomly censored samples and robust tests against one sided alternatives are also discussed. Their advantages and usefulness are demonstrated for the tests based on the minimum density power divergence estimators and illustrated with clinical trials and other medical data.

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Section: Future Extensions: Model Selection and Parametric (Cox) Regrmentioning

confidence: 99%

Robust Wald‐type tests under random censoring

Ghosh

Basu

Pardo

2020

Statistics in Medicine

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“…Moreover, survival data are often modeled using a member of location-scale and log-locationscale distributions. Weibull is one of the most practical distributions which is known to be helpful in various conditions [23]. As the result, based on the analysis obtained from the real case study, it is assumed that the output variable (survival times) follows Weibull distribution.…”

Section: Monitoring Procedures Based On Risk-adjusted Cusum Control Chartmentioning

confidence: 99%

Designing a risk-adjusted CUSUM control chart based on DEA and NSGA-II approaches (a case study in healthcare: Cardiovascular patients)

Rafiei

Asadzadeh

2020

Scientia Iranica

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Researchers have recently devoted a lot of attention to the development of control charts for monitoring healthcare systems. Accordingly, the purpose of this paper is to design a risk-adjusted cumulative sum (CUSUM) control chart to detect decreasing shifts. The proposed chart is used to monitor the survival times of patients who may be subject to an assignable cause such as human mistakes during a surgery. To this end, risk adjustment is performed to consider the impact of each patient's preoperative risks on survival times using survival analysis regression models. However, using the risk-adjusted CUSUM requires that the chart parameters are determined. Hence, a multi-objective economic-statistical model is proposed and a two-stage solution method including non-dominated sorting genetic algorithm (NSGA-II) and Data Envelopment Analysis (DEA) is implemented to solve the model and obtain the optimal design parameters. The performance of the proposed approach is also studied in a real cardiac surgery center. Finally, to confirm the effectiveness of the proposed multi-objective design, two comparisons with the bi-objective and pure economic designs are made. The results show that the performance of the risk-adjusted CUSUM obtained from the proposed model is better than the two other designs considering statistical and economic properties.

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“…For monitoring the lower Weibull percentiles using complete and type‐2 censored data, Wang et al 21 proposed EWMA and CUSUM control charts. Similarly, to monitor Weibull reliability data with dependent influential covariate, Asadzadeh and Baghaei 22 discussed the performance of CUSUM and EWMA control charts. In particular, the AFT models are integrated with robust regression techniques to model the relationship among variables in the presence of outliers.…”

Section: Introductionmentioning

confidence: 99%

“…Alma, 36 Multu and Sazak, 37 and Onur and Cetin 38 compared robust regression methods including M‐estimators, MM‐estimator, and S‐estimator against the ordinary least squared estimator. Similarly, Susanti et al 39 developed algorithms for M‐estimator, S‐estimator, and MM‐estimator in a robust regression modeling 22 . Kumar and Jaiswal 40 proposed a median‐based estimator to construct the phase ll control limits when the phase l data set contains outliers.…”

Section: Introductionmentioning

confidence: 99%

Robust proportional hazard‐based monitoring schemes for reliability data

Nabeel

Ali

Shah

2021

Quality & Reliability Eng

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Reliability of products is a key factor for successful businesses. In general, the existing monitoring schemes have poor performance as reliability data are often censored. Also, the products are manufactured in multistage processes where the outgoing quality gets affected by the previous stage quality. Besides this cascade property, historical data with outliers make the analysis even more complicated. This paper discusses exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for monitoring a two‐stage dependent process. In particular, the proportional hazard (PH) model is assumed for modeling the relationship of quality characteristics. Furthermore, to remove the detrimental effects of outliers on the results, robust regression method known as the M‐estimation is used. Besides a real case study, the performance of the proposed monitoring approach is assessed through a comprehensive simulation study. The results suggested that the EWMA chart outperforms the CUSUM chart.

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Robust AFT-based monitoring procedures for reliability data

Cited by 14 publications

References 22 publications

Robust Wald‐type tests under random censoring

Robust Wald‐type tests under random censoring

Designing a risk-adjusted CUSUM control chart based on DEA and NSGA-II approaches (a case study in healthcare: Cardiovascular patients)

Robust proportional hazard‐based monitoring schemes for reliability data

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