A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves
Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-11-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023880?viewType=HTML |
| _version_ | 1827689001144811520 |
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| author | Amal S. Hassan Najwan Alsadat Christophe Chesneau Ahmed W. Shawki |
| author_facet | Amal S. Hassan Najwan Alsadat Christophe Chesneau Ahmed W. Shawki |
| author_sort | Amal S. Hassan |
| collection | DOAJ |
| description | Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family. Discussions are held about the characteristics of the LBTLo-G family, including expressions for the probability density function, moments, and incomplete moments. In addition, different measures of uncertainty are determined. We provide four new sub-distributions and investigated their functionalities. Subsequently, a statistical analysis is given. The LBTLo-G family's parameter estimation is carried out using the maximum likelihood technique on the basis of full and censored samples. Simulation research is conducted to determine the parameters of the LBTLo Weibull (LBTLoW) distribution. Four genuine data sets are considered to illustrate the fitting behavior of the LBTLoW distribution. In each case, the application outcomes demonstrate that the LBTLoW distribution can, in fact, fit the data more accurately than other rival distributions. |
| first_indexed | 2024-03-10T10:09:44Z |
| format | Article |
| id | doaj.art-6c491b6b88454b96bce9437e05768cd2 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-10T10:09:44Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-6c491b6b88454b96bce9437e05768cd22023-11-22T01:18:53ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012011198711991110.3934/mbe.2023880A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reservesAmal S. Hassan0Najwan Alsadat 1Christophe Chesneau 2Ahmed W. Shawki31. Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt2. Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia3. Department of Mathematics, University de Caen Normandie, Campus Ⅱ, Caen 14032, France1. Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt4. Central Agency for Public Mobilization & Statistics (CAPMAS), Cairo, EgyptRecent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family. Discussions are held about the characteristics of the LBTLo-G family, including expressions for the probability density function, moments, and incomplete moments. In addition, different measures of uncertainty are determined. We provide four new sub-distributions and investigated their functionalities. Subsequently, a statistical analysis is given. The LBTLo-G family's parameter estimation is carried out using the maximum likelihood technique on the basis of full and censored samples. Simulation research is conducted to determine the parameters of the LBTLo Weibull (LBTLoW) distribution. Four genuine data sets are considered to illustrate the fitting behavior of the LBTLoW distribution. In each case, the application outcomes demonstrate that the LBTLoW distribution can, in fact, fit the data more accurately than other rival distributions.https://www.aimspress.com/article/doi/10.3934/mbe.2023880?viewType=HTMLweighted distributionincomplete momentstsallis measuremaximum likelihood estimationcensored samples |
| spellingShingle | Amal S. Hassan Najwan Alsadat Christophe Chesneau Ahmed W. Shawki A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves Mathematical Biosciences and Engineering weighted distribution incomplete moments tsallis measure maximum likelihood estimation censored samples |
| title | A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves |
| title_full | A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves |
| title_fullStr | A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves |
| title_full_unstemmed | A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves |
| title_short | A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves |
| title_sort | novel weighted family of probability distributions with applications to world natural gas oil and gold reserves |
| topic | weighted distribution incomplete moments tsallis measure maximum likelihood estimation censored samples |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023880?viewType=HTML |
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