Optimal analysis of adaptive type-II progressive censored for new unit-lindley model

Alrumayh, Amani; Weera, Wajaree; Khogeer, Hazar A.; Almetwally, Ehab M.

doi:10.1016/j.jksus.2022.102462

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…Other context of possible application of both estimators is reliability, where data coming from both laboratory tests and field observation are usually censored. Maximum-likelihood-based estimators have been proposed in the literature to deal with these data, see for example [ 52 , 53 ], which assume a type-II censored scheme, which differs from ours.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

et al. 2023

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Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

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Section: Discussionmentioning

confidence: 99%

“…estimators have been proposed in the literature to deal with these data, see for example [52,53], which assume a type-II censored scheme, which differs from ours.…”

Section: Plos Onementioning

confidence: 99%

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

et al. 2023

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“…The dataset shows the daily COVID-19 death rate gathered between October 1 and October 24, 2021. This dataset was first analyzed by Alrumayh et al . (2023), Alotaibi et al .…”

Section: Discussionmentioning

confidence: 99%

“…The dataset shows the daily COVID-19 death rate gathered between October 1 and October 24, 2021. This dataset was first analyzed by Alrumayh et al (2023), Alotaibi et al (2023), and it is seen that the dataset best fit to the UL model. The MLE, Kolmogorov-Smirnov (K-S), and p-value are 1.439, 0.158 and 0.5351, respectively.…”

Section: Optimal Censoring Schemementioning

confidence: 99%

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

Irfan,

Sharma

2023

IJQRM

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PurposeA progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.Design/methodology/approachIn this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.FindingsThe Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.Originality/valueThe ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.

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“…One is the data-filling method, such as substitution, standard statistical routines, various filling methods, and parameter estimation. [17][18][19] Alrumayh et al 20 used a Bayesian estimator to estimate the distribution and the parameters of the reliability function, which was verified on multiple datasets with censored samples. When there are few missing values, these kinds of operations are simple and popular, but in the case of numerous unknown data, they may lead to low accuracy of prediction.…”

Section: Introductionmentioning

confidence: 99%

RSF-based model for predicting pump failure trends in tunnels

Wu,

Chen,

et al. 2023

Advances in Mechanical Engineering

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The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.

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Optimal analysis of adaptive type-II progressive censored for new unit-lindley model

Cited by 14 publications

References 29 publications

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

Reliability characteristics of COVID-19 death rate using generalized progressive hybrid censored data

RSF-based model for predicting pump failure trends in tunnels

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