Bayesian and Non-Bayesian Estimation of the Nadaraj ah–Haghighi Distribution: Using Progressive Type-1 Censoring Scheme

Elbatal, İbrahim; Alotaibi, Naif; Alyami, Salem A.; Elgarhy, Mohammed; El-Saeed, Ahmed R.

doi:10.3390/math10050760

Cited by 12 publications

(11 citation statements)

References 21 publications

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“…Otherwise, the convergence condition is satisfied, and the algorithm ends. In the following, we review the steps of this algorithm, where i represents the ring index, θ represents the estimate, and T represents the threshold limit for the algorithm to end [41].…”

Section: Classification Of Indicatorsmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

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Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

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Section: Classification Of Indicatorsmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

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“…Assuming that the true lifetimes for some of the objects were unavailable, type-Ⅰ censoring scheme has been opted. This censoring scheme is associated with some pre-specified test termination time, see, (Kalbfleish and Prentice, 2011; Rabie and Li, 2020; and Elbatal et al, 2022).…”

Section: The Likelihood Function and The Posterior Distributionsmentioning

confidence: 99%

Bayesian Estimation of Transmuted Lomax Mixture Model with an Application to Type-I Censored Windshield Data

Mehdi¹,

Aslam²,

Feroze³

2022

Pak.j.stat.oper.res.

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Transmuted distributions have been centered of focus for researchers recently due to their flexibility and applicability in statistics. However, the only few contributions have considered estimation for mixture of transmuted lifetime models especially under Bayesian methods has been explored more recently. We have considered the Bayesian estimation of transmuted Lomax mixture model (TLMM) for type-I censored samples. The Bayes estimates (BEs) for informative and non-informative priors. The BEs and posterior risks (PRs) are evaluated using four different loss functions (LFs), two symmetric and two asymmetric, namely the squared error loss function (SELF), precautionary loss function (PLF), weighted balance loss function (WBLF), and general entropy loss function (GELF). Simulations are run using Lindley Approximation method to compare the BEs under various sample sizes and censoring rates. The estimates under informative prior and GELF were found superior to their counterparts. The applicability of the proposed estimates has been illustrated using the analysis of a real data regarding type-I censored failure times of windshields airplanes.

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“…Helmy et al [24] investigated Shannon entropy estimation of Lomax distribution using unified hybrid censored data. Bayesian and non-Bayesian estimation of the Nadarajah-Haghighi distribution using progressive Type-1 censoring scheme studied by Elbatal et al [25].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Entropy for Log‐Logistic Distribution under Progressive Type II Censoring

Shrahili

El-Saeed

Hassan

et al. 2022

Journal of Nanomaterials

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Entropy is a useful indicator of information content that has been used in a number of applications. The Log-Logistic (LL) distribution is a probability distribution that is often employed in survival analysis. This paper addresses the problem of estimating multiple entropy metrics for an LL distribution using progressive type II censoring. We derive formulas for six different types of entropy measurements. To obtain the estimators of the proposed entropy measures, the maximum likelihood approach is applied. Approximate confidence intervals are calculated for the entropy metrics under discussion. A numerical evaluation is performed using various censoring methods and sample sizes to characterize the behavior of estimator’s measures using relative biases, related mean squared errors, average interval lengths, and coverage probabilities. Numerical analysis revealed that the accuracy measures improve with sample size, and the suggested entropy estimates approach the genuine values as censoring levels decrease. Finally, an actual dataset was evaluated for demonstration purposes.

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Bayesian and Non-Bayesian Estimation of the Nadaraj ah–Haghighi Distribution: Using Progressive Type-1 Censoring Scheme

Cited by 12 publications

References 21 publications

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Bayesian Estimation of Transmuted Lomax Mixture Model with an Application to Type-I Censored Windshield Data

Estimation of Entropy for Log‐Logistic Distribution under Progressive Type II Censoring

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