In this study, a new approach to transmutation theory is developed by using negative dependency basement. Once choosing a distribution that has negative dependency with the same marginal, a new bivariate distribution is derived. In this study, we examined a new transmutation technique in which a negative dependency offers a big success in modeling rather than most known and used statistical distributions. This approach clash with classical transmutation methods. In this study at the beginning, the classical transmutation is defined. Later, we introduce the new technique and obtain lower and upper bounds of distribution to show that this approach gives us a distribution. Gaining new bivariate continuous distributions with this technique may be more appropriate in theory, and modeling of some data sets in terms of this approach may be more efficient.
Transmuted bivariate distribution Dependence Bivariate distribution Negative dependency Fréchet bounds
Primary Language | English |
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Subjects | Statistics |
Journal Section | Natural Sciences |
Authors | |
Publication Date | December 29, 2020 |
Submission Date | May 24, 2020 |
Acceptance Date | August 19, 2020 |
Published in Issue | Year 2020Volume: 41 Issue: 4 |