A Bayesian semiparametric regression model for reliability data using effective age

Li, Li; Hanson, Timothy

doi:10.1016/j.csda.2013.11.015

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Claudio et al [48] propose a maximum likelihood estimation with time-dependent covariates in the case of the Linear Extended Yule Process, which is an imperfect maintenance model different from the virtual age models. Li and Hanson [49] use Kijima's models along with step-wise constant covariates, but the covariates act on the maintenance effect, which will not be the case in this paper. Moreover, they use Bayesian inference.…”

Section: Contextmentioning

confidence: 99%

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Brenière

Doyen

Bérenguer

2020

Reliability Engineering & System Safety

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A repairable system faces some failures and imperfect maintenances throughout its lifetime. If several identical and independent systems are considered together, some differences may arise between the systems, such as the geographical location or the maintenance team for example, which are constant information, or the weather conditions, which vary with time. This observed heterogeneity will influence more or less the failure process. In this paper, we include these data in a generalized virtual age model with the use of covariates. Then we estimate simultaneously the effect of the maintenances, that of the covariates, and the intrinsic wear of the systems. We also propose two simulation methods as well as a numerical estimation procedure. Then we assess the quality of the estimation of the parameters with a thorough simulation study.

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Section: Contextmentioning

confidence: 99%

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Brenière

Doyen

Bérenguer

2020

Reliability Engineering & System Safety

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“…(2) The data mining results are not all "equality constraints" like conditional functional dependencies, which cannot guide the repair process directly, but also need to integrate a decision tree [23], interpolation [24] or other methods. The other is to use the dataset itself to repair data errors, e.g., Reference [25] proposes a data cleaning framework, Boostclean, based on machine learning, which improves the accuracy of detection and repair, and Reference [26] proposed a regression-based method to repair the erroneous data in datasets, while using an interactive method to correct the unsatisfactory data, which combines the supervised and unsupervised ideas. These kinds of methods do not require an additional training set in the repair processes, but the time complexity is relatively high.…”

Section: Research Statusmentioning

confidence: 99%

When Considering More Elements: Attribute Correlation in Unsupervised Data Cleaning under Blocking

Dai

Wang

2019

Symmetry

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In banks, governments, and internet companies, due to the increasing demand for data in various information systems and continuously shortening of the cycle for data collection and update, there may be a variety of data quality issues in a database. As the expansion of data scales, methods such as pre-specifying business rules or introducing expert experience into a repair process are no longer applicable to some information systems requiring rapid responses. In this case, we divided data cleaning into supervised and unsupervised forms according to whether there were interventions in the repair processes and put forward a new dimension suitable for unsupervised cleaning in this paper. For weak logic errors in unsupervised data cleaning, we proposed an attribute correlation-based (ACB)-Framework under blocking, and designed three different data blocking methods to reduce the time complexity and test the impact of clustering accuracy on data cleaning. The experiments showed that the blocking methods could effectively reduce the repair time by maintaining the repair validity. Moreover, we concluded that the blocking methods with a too high clustering accuracy tended to put tuples with the same elements into a data block, which reduced the cleaning ability. In summary, the ACB-Framework with blocking can reduce the corresponding time cost and does not need the guidance of domain knowledge or interventions in repair, which can be applied in information systems requiring rapid responses, such as internet web pages, network servers, and sensor information acquisition.

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“…Only a few papers deal with the introduction of covariates in virtual age models. Li and Hanson 12 have considered that covariates can have an effect on maintenance efficiency through age reduction. Doyen et al 6 have studied the introduction of constant covariates in the initial intensity.…”

mentioning

confidence: 99%

Discussion of “Virtual age, is it real?”

Doyen

Gaudoin

2020

Appl Stoch Models Bus & Ind

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A Bayesian semiparametric regression model for reliability data using effective age

Cited by 6 publications

References 40 publications

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

When Considering More Elements: Attribute Correlation in Unsupervised Data Cleaning under Blocking

Discussion of “Virtual age, is it real?”

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