Maximum Likelihood Estimation in the Inverse Weibull Distribution with Type II Censored Data

Alshaikh, Fatima A.; Baklizi, Ayman

doi:10.13189/ms.2022.100616

Cited by 3 publications

(2 citation statements)

References 9 publications

(11 reference statements)

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“…Definition 4 (see Shafiq and Viertl (2014)): A subset x of the set of real numbers (denoted by R) is named fuzzy number and is characterized by the so-called membership function µ x(. ).…”

Section: Preliminary Concepts Of Fuzzy Sets Theorymentioning

confidence: 99%

Untitled

2024

SiTns

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Statistics in Transition new series (SiTns) is an international journal published jointly by the Polish Statistical Association (PTS) and Statistics Poland, on a quarterly basis (during 1993-2006 it was issued twice and since 2006 three times a year). Also, it has extended its scope of interest beyond its originally primary focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy through embracing questions related to systemic transformations of and within the national statistical systems, world-wide.The SiTns seeks contributors that address the full range of problems involved in data production, data dissemination and utilization, providing international community of statisticians and users -including researchers, teachers, policy makers and the general public -with a platform for exchange of ideas and for sharing best practices in all areas of the development of statistics.Accordingly, articles dealing with any topics of statistics and its advancement -as either a scientific domain (new research and data analysis methods) or as a domain of informational infrastructure of the economy, society and the state -are appropriate for Statistics in Transition new series.Demonstration of the role played by statistical research and data in economic growth and social progress (both locally and globally), including better-informed decisions and greater participation of citizens, are of particular interest.Each paper submitted by prospective authors are peer reviewed by internationally recognized experts, who are guided in their decisions about the publication by criteria of originality and overall quality, including its content and form, and of potential interest to readers (esp. professionals).

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“…Definition 4 (see Shafiq and Viertl (2014)): A subset x of the set of real numbers (denoted by R) is named fuzzy number and is characterized by the so-called membership function µ x(. ).…”

Section: Preliminary Concepts Of Fuzzy Sets Theorymentioning

confidence: 99%

Untitled

2024

SiTns

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“…Alshaikh and Baklizi [8] investigated maximum likelihood estimation in the context of the inverse Weibull distribution with type II censored data. Jana and Bera [31] explored the estimation of parameters in the IWD and its application to a multi-component stress-strength model.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application

Hasaballah,

A. Tashkandy,

Balogun

et al. 2024

Eksploatacja i Niezawodność – Maintenance and Reliability

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In this study, the unified hybrid censored approach is employed to estimate the parameters of the inverse Weibull distribution, as well as the survival and hazard rate functions. Parameter estimates are obtained using both Bayesian and Maximum Likelihood approaches, with Bayesian estimates acquired through Lindley's approximation method using three distinct balanced loss functions. These encompass both symmetric and asymmetric balanced loss functions, specifically the balanced squared error (BSE) loss function, the balanced linear exponential (BLINEX) loss function, and the balanced general entropy (BGE) loss function. We conduct a simulation study to compare the effectiveness of various estimators, and a real-world data analysis is presented to illustrate practical implementation. Ultimately, our findings indicate that Bayesian parameter estimates consistently outperform their Maximum Likelihood counterparts across all methods.

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On Bayesian inference of reliability parameter in Burr-type XII model based on imprecise data: a survey on fuzzy modelling

Makhdoom,

Pak

2024

SiTns

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There are always two major sources of uncertainty in measurements related to lifetime surveys: variation among the observations and imprecision of individual observation called fuzziness. The typical statistical analysis is based on variation among the observations and does not consider the imprecision due to individual observation. However, ignoring the imprecision of individual observations may cause losing information and getting misleading results. It is mandatory to analyse such data, to extend the real numbers classically and Bayesian estimation methods to fuzzy numbers. Inference on the Burr-type (BT) XII model, based on precise measurements, is carried out by researchers, yet the problem of estimating parameters, in the presence of fuzzy data, remains unresolved. We are estimating the BT XII distribution parameters and their corresponding reliability when the available data are in the fuzzy numbers. The maximum likelihood estimation (MLE), the Bayesian method and the method of moments are used for estimating parameters. Finally, these estimators are compared via a Monte-Carlo simulation study.

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Maximum Likelihood Estimation in the Inverse Weibull Distribution with Type II Censored Data

Cited by 3 publications

References 9 publications

Untitled

Untitled

Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application

On Bayesian inference of reliability parameter in Burr-type XII model based on imprecise data: a survey on fuzzy modelling

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