Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data

Soliman, Ahmed A.; Abdellah, Ahmed H.; Abou-Elheggag, N. A.; Modhesh, A. A.

doi:10.1080/02664763.2012.725466

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…In Table (3) , relief times (in hours) are shown for 50 arthritic patients treated with a fixed dosage of this medication. These data were first utilized by [ 30 ] and later by [ 31 ].…”

Section: Real Life Datamentioning

confidence: 99%

The principle of maximum entropy and the probability-weighted moments for estimating the parameters of the Kumaraswamy distribution

Helu

2022

PLoS ONE

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Since Shannon’s formulation of the entropy theory in 1940 and Jaynes’ discovery of the principle of maximum entropy (POME) in 1950, entropy applications have proliferated across a wide range of different research areas including hydrological and environmental sciences. In addition to POME, the method of probability-weighted moments (PWM), was introduced and recommended as an alternative to classical moments. The PWM is thought to be less impacted by sampling variability and be more efficient at obtaining robust parameter estimates. To enhance the PWM, self-determined probability-weighted moments was introduced by (Haktanir 1997). In this article, we estimate the parameters of Kumaraswamy distribution using the previously mentioned methods. These methods are compared to two older methods, the maximum likelihood and the conventional method of moments techniques using Monte Carlo simulations. A numerical example based on real data is presented to illustrate the implementation of the proposed procedures.

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“…In Table (3) , relief times (in hours) are shown for 50 arthritic patients treated with a fixed dosage of this medication. These data were first utilized by [ 30 ] and later by [ 31 ].…”

Section: Real Life Datamentioning

confidence: 99%

The principle of maximum entropy and the probability-weighted moments for estimating the parameters of the Kumaraswamy distribution

Helu

2022

PLoS ONE

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“…In the simulation study of the difference of SNRs, the sample sizes were (n, m) = (10, 10), (10,20), (20,20), (20,30), (30, 30), (30, 50), (50, 50), (50, 100), (100, 100), (100, 200), and (200, 200); the population means were µ X = µ T = 1; and the population SNRs were (θ X , θ T ) = (10, 1), (10, 2), (10,5), and (10,10) for the normally distributed data. Therefore, the population standard deviations of the normally distributed data σ X = log((1/θ 2 X ) + 1) and σ T = log((1/θ 2 T ) + 1) were computed.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…In image processing, the ratio of the mean pixel values over a given neighborhood is calculated by the SNR of an image. Furthermore, the SNR is used for the analysis of portfolio selection models and market risk (see [4,5]).…”

Section: Introductionmentioning

confidence: 99%

Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Thangjai

Niwitpong

2019

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In this article, we propose approaches for constructing confidence intervals for the single signal-to-noise ratio (SNR) of a log-normal distribution and the difference in the SNRs of two log-normal distributions. The performances of all of the approaches were compared, in terms of the coverage probability and average length, using Monte Carlo simulations for varying values of the SNRs and sample sizes. The simulation studies demonstrate that the generalized confidence interval (GCI) approach performed well, in terms of coverage probability and average length. As a result, the GCI approach is recommended for the confidence interval estimation for the SNR and the difference in SNRs of two log-normal distributions.

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“…For this reason, Wu and Kus ¸ [30] introduced a life testing scheme, which combines first-failure censoring with a progressive type-II censoring called a progressive first-failure censoring (Pro-F-F-C) scheme. Many previous studies have discussed inference under a Pro-F-F-C scheme for different lifetime distributions, for example, Weibull by Wu and Kus ¸ [30], Burr Type XII by Soliman et al [31,32], Gompertz by Soliman et al [33], Lomax by Mahmoud et al [34], Compound Rayleigh by Abushal [35], Generalized Inverted Exponential by Ahmed [36], the Mixture of Weibull and Lomax by Mahmoud et al [37], and exponentiated Frechet by Soliman et al [38]. Recently, Cai and Gui [39] discussed the classical and Bayesian inference for a Pro-F-F-C lefttruncated normal distribution.…”

Section: Introductionmentioning

confidence: 99%

Inferences for Generalized Pareto Distribution Based on Progressive First‐Failure Censoring Scheme

2021

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In this article, we consider estimation of the parameters of a generalized Pareto distribution and some lifetime indices such as those relating to reliability and hazard rate functions when the failure data are progressive first-failure censored. Both classical and Bayesian techniques are obtained. In the Bayesian framework, the point estimations of unknown parameters under both symmetric and asymmetric loss functions are discussed, after having been estimated using the conjugate gamma and discrete priors for the shape and scale parameters, respectively. In addition, both exact and approximate confidence intervals as well as the exact confidence region for the estimators are constructed. A practical example using a simulated data set is analyzed. Finally, the performance of Bayes estimates is compared with that of maximum likelihood estimates through a Monte Carlo simulation study.

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Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data

Cited by 14 publications

References 35 publications

The principle of maximum entropy and the probability-weighted moments for estimating the parameters of the Kumaraswamy distribution

The principle of maximum entropy and the probability-weighted moments for estimating the parameters of the Kumaraswamy distribution

Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Inferences for Generalized Pareto Distribution Based on Progressive First‐Failure Censoring Scheme

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