Reliability estimation in Rayleigh distribution based on fuzzy lifetime data

Pak, Abbas; Parham, Gholam Ali; Saraj, Mansour

doi:10.1007/s13198-013-0190-5

Cited by 31 publications

(16 citation statements)

References 12 publications

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“…Keeping in view importance of fuzziness, in the last couple of decades, a partial amount of work related to fuzzy lifetime data has been published like fuzzy Bayesian estimation on lifetime data, Bayesian reliability analysis for fuzzy lifetime data, on reliability estimation based on fuzzy lifetime data, reliability estimation in Rayleigh distribution for fuzzy lifetime data, and empirical reliability functions based on fuzzy lifetime data …”

Section: Lifetime Analysismentioning

confidence: 99%

Generalized Likelihood Ratio Test and Cox'sF-Test Based on Fuzzy Lifetime Data

Shafiq

Atif

Viertl

2016

Int. J. Intell. Syst.

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Recent developments in measurement science show that continuous measurements are no more precise numbers but more or less imprecise and are called fuzzy. Therefore, to utilize this imprecision of observations, the corresponding analysis techniques related to continuous quantities are essential to generalize fuzzy observations. This study is aimed to generalize the likelihood ratio test and Cox's F‐test for fuzzy observations in such a way that they are able to integrate fuzziness of lifetime observations for the inference in addition to stochastic variation. The proposed generalized tests are best suited for lifetime analysis as these cover fuzziness of single observations in addition to random variation.

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Section: Lifetime Analysismentioning

confidence: 99%

Generalized Likelihood Ratio Test and Cox'sF-Test Based on Fuzzy Lifetime Data

Shafiq

Atif

Viertl

2016

Int. J. Intell. Syst.

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“…In addition, almost all the recent contributions regarding analysis of MHS in Pakistan have utilized classical models for investigation of the important determinants of maternal healthcare in the country. On the other hand, some of the studies have explored that the Bayesian methods often produce better results as compared to classical methods [ 19 – 21 ]. The main feature of Bayes methods is that they allow us to incorporate the prior information regarding the parameters of the concerned models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

Feroze

Ziad

Fayyaz

et al. 2021

Computational and Mathematical Methods in Medicine

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Objectives. This study is aimed at investigating the time trends and disparities in access to maternal healthcare in Pakistan using Bayesian models. Study Design. A longitudinal study from 2006 to 2018. Methods. The detailed analysis is based on the data from Pakistan Demographic and Health Survey (PDHS) conducted during 2006-2018. We have proposed Bayesian logistic regression models (BLRM) to investigate the trends of maternal healthcare in the country. Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (CLRM). Results. The results from the analysis suggested that BLRM perform better than CLRM. The access to antenatal healthcare increased from 61% to 86% during years 2006-18. The utilization of medication also improved from 44% in 2006 to 60% in 2018. Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018. Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery. Additionally, the likelihood of MHS provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of Sindh and Punjab. Conclusions. The gaps in utilization of MHS in different socioeconomic groups of the society have not decreased significantly during 2006-2018. Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

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“…Subsequently, in addition to classical statistical methodology, fuzzy models are additionally necessary to analyze realistic lifetime data. Realizing the importance of fuzziness research has been conducted from the last couple of decades, [20][21][22][23][24][25][26][27][28][29][30][31][32][33] but still most of the times fuzziness of the individual observations is ignored, which leads to non-representative estimates. For threeparameter log-normal distribution the parameter estimates based on fuzzy life times are presented in Shafiq et al; 34 therefore, in this article, generalized (fuzzy) estimators for three-parameter lifetime distributions, that is, Weibull, Pareto, and Gamma are proposed.…”

Section: Lifetime Analysismentioning

confidence: 99%

Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times

Shafiq

Atif

Viertl

2017

Advances in Mechanical Engineering

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Besides errors and stochastic variation, the observed data always have more or less imprecision. In contrast, classical statistical procedures are based on precise measurements and do not allow imprecision of the individual observations. Therefore, to integrate imprecision of the observations, classical statistical techniques are required to generalize for fuzzy numbers. Since lifetime measurements are also more or less fuzzy, therefore, this study was aimed to generalize parameters and reliability functions estimation for the three-parameter lifetime distributions in such manners to integrate fuzziness of the lifetime data. The obtained estimators assimilate fuzziness and stochastic variation of lifetime observations, whereas classical techniques are only based on stochastic variation. This propagation of fuzziness makes the proposed estimators more realistic and suitable for lifetime analysis. Therefore, in order to model lifetime data, the suggested techniques are far better alternative.

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Reliability estimation in Rayleigh distribution based on fuzzy lifetime data

Cited by 31 publications

References 12 publications

Generalized Likelihood Ratio Test and Cox'sF-Test Based on Fuzzy Lifetime Data

Generalized Likelihood Ratio Test and Cox'sF-Test Based on Fuzzy Lifetime Data

Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times

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