A new generalized inverse Gaussian distribution with Bayesian estimators
Abstract A new family of distributions, called the transformed inverse Gaussian (TIG) distribution with four parameters, is introduced. Within this family, a specific distribution called the new generalized inverse Gaussian (NGIG) distribution, with three parameters, is examined in depth. Two distin...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Data |
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| Online Access: | https://doi.org/10.1007/s44248-025-00066-y |
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| _version_ | 1849234822792216576 |
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| author | Kenneth Goward Chin-I Cheng Kahadawala Cooray Keshab Raj Dahal |
| author_facet | Kenneth Goward Chin-I Cheng Kahadawala Cooray Keshab Raj Dahal |
| author_sort | Kenneth Goward |
| collection | DOAJ |
| description | Abstract A new family of distributions, called the transformed inverse Gaussian (TIG) distribution with four parameters, is introduced. Within this family, a specific distribution called the new generalized inverse Gaussian (NGIG) distribution, with three parameters, is examined in depth. Two distinct parameterizations for the NGIG distribution are presented, and the advantages, both computational and theoretical, of one parameterization over the other are discussed. The paper delves into maximum likelihood estimation techniques and compares them with Bayesian methods using Jeffreys-type priors for parameter estimation. It also establishes the propriety of the posterior distribution given certain conditions with this prior. The practical applicability of this distribution is demonstrated using real-world data. |
| format | Article |
| id | doaj-art-14033d4e64e3422e914eb0dff30d7761 |
| institution | Kabale University |
| issn | 2731-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Data |
| spelling | doaj-art-14033d4e64e3422e914eb0dff30d77612025-08-20T04:03:01ZengSpringerDiscover Data2731-69552025-07-013111610.1007/s44248-025-00066-yA new generalized inverse Gaussian distribution with Bayesian estimatorsKenneth Goward0Chin-I Cheng1Kahadawala Cooray2Keshab Raj Dahal3Department of Mathematics and Statistics, University of North FloridaDepartment of Statistics, Actuarial and Data Sciences, Central Michigan UniversityDepartment of Statistics, Actuarial and Data Sciences, Central Michigan UniversityDepartment of Mathematics, State University of New York CortlandAbstract A new family of distributions, called the transformed inverse Gaussian (TIG) distribution with four parameters, is introduced. Within this family, a specific distribution called the new generalized inverse Gaussian (NGIG) distribution, with three parameters, is examined in depth. Two distinct parameterizations for the NGIG distribution are presented, and the advantages, both computational and theoretical, of one parameterization over the other are discussed. The paper delves into maximum likelihood estimation techniques and compares them with Bayesian methods using Jeffreys-type priors for parameter estimation. It also establishes the propriety of the posterior distribution given certain conditions with this prior. The practical applicability of this distribution is demonstrated using real-world data.https://doi.org/10.1007/s44248-025-00066-yBayesian analysisInverse Gaussian distributionMaximum likelihood estimationMetropolis-Hastings algorithm |
| spellingShingle | Kenneth Goward Chin-I Cheng Kahadawala Cooray Keshab Raj Dahal A new generalized inverse Gaussian distribution with Bayesian estimators Discover Data Bayesian analysis Inverse Gaussian distribution Maximum likelihood estimation Metropolis-Hastings algorithm |
| title | A new generalized inverse Gaussian distribution with Bayesian estimators |
| title_full | A new generalized inverse Gaussian distribution with Bayesian estimators |
| title_fullStr | A new generalized inverse Gaussian distribution with Bayesian estimators |
| title_full_unstemmed | A new generalized inverse Gaussian distribution with Bayesian estimators |
| title_short | A new generalized inverse Gaussian distribution with Bayesian estimators |
| title_sort | new generalized inverse gaussian distribution with bayesian estimators |
| topic | Bayesian analysis Inverse Gaussian distribution Maximum likelihood estimation Metropolis-Hastings algorithm |
| url | https://doi.org/10.1007/s44248-025-00066-y |
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