Wang, Frank Xuyan
(2019):
*Shape Factor Asymptotic Analysis I.*

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## Abstract

The shape factor defined as kurtosis divided by skewness squared K/S^2 is characterized as the only choice among all factors K/〖|S|〗^α ,α>0 which is greater than or equal to 1 for all probability distributions. For a specific distribution family, there may exists α>2 such that min〖K/〖|S|〗^α 〗≥1. The least upper bound of all such α is defined as the distribution’s characteristic number. The useful extreme values of the shape factor for various distributions which are found numerically before, the Beta, Kumaraswamy, Weibull, and GB2 Distribution, are derived using asymptotic analysis. The match of the numerical and the analytical results can be considered prove of each other. The characteristic numbers of these distributions are also calculated. The study of the boundary value of the shape factor, or the shape factor asymptotic analysis, help reveal properties of the original shape factor, and reveal relationship between distributions, such as between the Kumaraswamy distribution and the Weibull distribution.

Item Type: | MPRA Paper |
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Original Title: | Shape Factor Asymptotic Analysis I |

Language: | English |

Keywords: | Shape Factor, Skewness, Kurtosis, Asymptotic Expansion, Beta Distribution, Kumaraswamy Distribution, Weibull Distribution, GB2 Distribution, Computer Algebra System, Numerical Optimization, Characteristic Number. |

Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C46 - Specific Distributions ; Specific Statistics C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies |

Item ID: | 93357 |

Depositing User: | Dr Frank Xuyan Wang |

Date Deposited: | 24 Apr 2019 02:56 |

Last Modified: | 27 Sep 2019 09:49 |

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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/93357 |