Document Details
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Document Type |
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Thesis |
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Document Title |
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COMBINING TWO EXPONENTIATED FAMILIES TO GENERATE A NEW FAMILY OF DISTRIBUTIONS دمج عائلتين أسيتين لتوليد عائلة جديدة من التوزيعات |
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Subject |
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Faculty of Science |
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Document Language |
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Arabic |
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Abstract |
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Numerous studies have demonstrated the importance of statistical distributions in modeling and analyzing real-life data sets. In order to analyze, describe, and predict data accurately, it is necessary to use distributions with high flexibility in modeling. Due to the inflexibility of most classical distributions, researchers are seeking more general distributions with more flexibility to analyze complex data. Statistical distributions can be generalized using various techniques, such as adding parameters to the distribution, using generators, or combining distributions.
This thesis proposes a new method for generating new families of distributions. In particular, this method is based on the combination of two well-known generators, namely the exponentiated approach and the exponentiated T-X approach. Three baseline distributions were generalized by applying the proposed method, the exponential, Rayleigh, and inverse Rayleigh distributions. Among the characteristics of the suggested distributions is
their ability to improve the flexibility for modeling real data. In other words, the probability distribution functions and the hazard functions for the members of the proposed family take many different forms. For each distribution, some of the statistical properties were studied such as the quantile, median, moments, moment generating function, characteristics function, R'enyi entropy and order statistics. Further, the maximum likelihood estimation method has been applied to estimate the unknown parameters of each distribution. Subsequently, the estimators have been evaluated through simulation studies. As a means of examining the efficiency of the proposed distributions, they were applied to several sets of real data.This analysis reveals that the proposed family members are more flexible compared to some
competing distributions. |
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Supervisor |
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Dr. Dawlat al-Sulami |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1444 AH
2023 AD |
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Added Date |
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Monday, July 3, 2023 |
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Researchers
| اعتماد رزين السلمي | Al-Sulami, Itimad Razeen | Researcher | Master | |
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