Statistical Inferences of Type-II Progressively Hybrid Censored Fuzzy Data with Rayleigh Distribution

Chaturvedi, Ankita; Singh, Sanjay Kumar; Singh, Umesh

doi:10.17713/ajs.v47i3.752

Cited by 7 publications

(2 citation statements)

References 31 publications

(32 reference statements)

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“…Pak, Parham, and Saraj (2013b), Pak, Parham, and Saraj (2013a), Pak, Parham, and Saraj (2014a), Pak and Chatrabgoun (2016) discussed the inferential procedures of a number of lifetime distributions under both classical and Bayesian setup using complete as well as censored fuzzy data. Recently, Chaturvedi, Singh, and Singh (2018) obtained the inferences of type-II progressively hybrid censored fuzzy data using Rayleigh distribution.…”

Section: Introductionmentioning

confidence: 99%

Maximum Product Spacings Estimator for Fuzzy Data Using Inverse Lindley Distribution

Chaturvedi

Singh

2023

AJS

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The article addresses the problem of parameter estimation of the inverse Lindley distribution when the observations are fuzzy. The estimation of the unknown model parameter was performed using both classical and Bayesian methods. In the classical approach, the estimation of the population parameter is performed using the maximum likelihood (ML) method and the maximum product of distances (MPS) method. In the Bayesian setup, the estimation is obtained using the squared error loss function (SELF) with the Markov Chain Monte Carlo (MCMC) technique. Asymptotic confidence intervals and highest posterior density (HPD) credible intervals for the unknown parameter are also obtained. The performances of the estimators are compared based on their MSEs. Finally, a real data set is analyzed for numerical illustration of the above estimation methods.

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Section: Introductionmentioning

confidence: 99%

Maximum Product Spacings Estimator for Fuzzy Data Using Inverse Lindley Distribution

Chaturvedi

Singh

2023

AJS

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“…The fuzzy statistical approach in this research is an analysis phenomenon under uncertain circumstances. The Azerbaijani scientist Zadeh in 1965 developed the concept of fuzzy groups theory where each element belongs to the fuzzy groups according to a specific membership [1], [2]. Survival functions are the most essential and well-known statistical functions that are widely used to estimate the survival probability for people suffering from specific diseases.…”

Section: Introductionmentioning

confidence: 99%

M.L. Estimator for Fuzzy Survival Function to the Kidney Failure Patients

Alnaqash

Sahib

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The dependence of traditional statistical methods in finding estimators and testing hypotheses depends on data that take specific values. However, uncertainty (Fuzzy) appears in most data, including survival time data, which requires researchers to apply Fuzzy methods in general when finding estimators, particularly the estimators of a survival function. In this research, the experimental method (simulation) was used to compare Fuzzy methods (definition of fuzzy logic, cut level -δ, Buckley) at three fuzzy degrees (0, 0.5, 1), for selected survival times (t = 2, 4, 6, 8, 10, 12) in days. To determine the best fuzzy method used to find the Maximum Likelihood Estimators of the fuzzy survival function depends on the Avery Mean Squares Error (AMES) for the lower and upper bound estimators of the fuzzy survival function for each method. From the experimental method, we came to an advantage of defining fuzzy logic for the maximum likelihood estimator for the survival function of Weibull distribution (distribution of survival times data in the research). The maximum likelihood estimator calculated according to the method of defining fuzzy logic for patients' survival times with kidney failure collected from sections of the dialysis department in educational hospitals (Baghdad, Al-Kindi, Al-Karama, Al-Kadhimiya). This research found that the probability of fuzzy survival for two days to the patients with kidney failure reaches 0.7504 and this probability decreases with an increase in the time of stay until it reaches 0.07704 the probability of staying for a month.

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