Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations
This study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data. To manage these complexities, both the Maximum Likelihood and Bayesian estimation techniques are employed....
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11023544/ |
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| author | Vijay Kumar Lingutla Nagamani Nadiminti |
| author_facet | Vijay Kumar Lingutla Nagamani Nadiminti |
| author_sort | Vijay Kumar Lingutla |
| collection | DOAJ |
| description | This study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data. To manage these complexities, both the Maximum Likelihood and Bayesian estimation techniques are employed. Because of the absence of closed-form solutions for the Maximum Likelihood estimators, the Expectation-Maximization algorithm is utilized, and asymptotic confidence intervals are constructed based on the observed information matrix. For Bayesian estimation, a conjugate prior is used to derive Bayes estimators, which are approximated using Lindley’s method in light of the analytical intractability. Gibbs sampling was implemented to estimate posterior densities and construct the Highest Posterior Density intervals. Approximate Bayesian Computation is also employed as a likelihood-free approach to Bayesian inference, which is particularly useful under fuzzy data conditions where the likelihood is difficult to specify explicitly. A comprehensive comparison of the Maximum Likelihood Estimation, Lindley’s approximation, Approximate Bayesian Computation, and Gibbs sampling is conducted to evaluate their performance. The effectiveness of the proposed method was further illustrated using real data from a light-emitting diode manufacturing process. |
| format | Article |
| id | doaj-art-a7d4c37f967f4f8a85d509906980d3b4 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a7d4c37f967f4f8a85d509906980d3b42025-08-20T03:21:34ZengIEEEIEEE Access2169-35362025-01-011310040510041810.1109/ACCESS.2025.357638411023544Fuzzy Data Modeling and Parameter Estimation in Two Gamma PopulationsVijay Kumar Lingutla0https://orcid.org/0009-0005-6449-2205Nagamani Nadiminti1https://orcid.org/0000-0002-5518-6653Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, IndiaThis study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data. To manage these complexities, both the Maximum Likelihood and Bayesian estimation techniques are employed. Because of the absence of closed-form solutions for the Maximum Likelihood estimators, the Expectation-Maximization algorithm is utilized, and asymptotic confidence intervals are constructed based on the observed information matrix. For Bayesian estimation, a conjugate prior is used to derive Bayes estimators, which are approximated using Lindley’s method in light of the analytical intractability. Gibbs sampling was implemented to estimate posterior densities and construct the Highest Posterior Density intervals. Approximate Bayesian Computation is also employed as a likelihood-free approach to Bayesian inference, which is particularly useful under fuzzy data conditions where the likelihood is difficult to specify explicitly. A comprehensive comparison of the Maximum Likelihood Estimation, Lindley’s approximation, Approximate Bayesian Computation, and Gibbs sampling is conducted to evaluate their performance. The effectiveness of the proposed method was further illustrated using real data from a light-emitting diode manufacturing process.https://ieeexplore.ieee.org/document/11023544/Gamma distributionsfuzzy datamaximum likelihood estimationBayesian estimationLindley approximationGibbs sampling |
| spellingShingle | Vijay Kumar Lingutla Nagamani Nadiminti Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations IEEE Access Gamma distributions fuzzy data maximum likelihood estimation Bayesian estimation Lindley approximation Gibbs sampling |
| title | Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations |
| title_full | Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations |
| title_fullStr | Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations |
| title_full_unstemmed | Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations |
| title_short | Fuzzy Data Modeling and Parameter Estimation in Two Gamma Populations |
| title_sort | fuzzy data modeling and parameter estimation in two gamma populations |
| topic | Gamma distributions fuzzy data maximum likelihood estimation Bayesian estimation Lindley approximation Gibbs sampling |
| url | https://ieeexplore.ieee.org/document/11023544/ |
| work_keys_str_mv | AT vijaykumarlingutla fuzzydatamodelingandparameterestimationintwogammapopulations AT nagamaninadiminti fuzzydatamodelingandparameterestimationintwogammapopulations |