A Gamma–normal series truncation approximation for computing the Weibull renewal function |
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Authors: | R Jiang |
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Affiliation: | aFaculty of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan 410076, China |
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Abstract: | This paper presents a series truncation approximation for computing the Weibull renewal function. In the proposed model, the n-fold convolution of the Weibull Cdf is approximated by a mixture of the n-fold convolutions of Gamma and normal Cdfs. The mixture weight can be optimally determined and fitted into a very accurate linear function of Weibull shape parameter β. Major advantages of the proposed model include:- (a) The proposed model and its parameters can be directly written out. Using the proposed model, the renewal density and variance functions can be easily evaluated.
- (b) The proposed model includes Gamma and normal series truncation models as its special cases. It is easy to be implemented in Excel. The series converges fairly fast.
- (c) Over the range of β(0.87,8.0), the maximum absolute error is smaller than 0.01; and over , the maximum absolute error is smaller than 0.0037.
- (d) The model can be easily extended to non-Weibull case with some additional work.
Keywords: Renewal function; Renewal density; Variance of number of renewals; Weibull distribution; Gamma distribution; Normal distribution |
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Keywords: | Renewal function Renewal density Variance of number of renewals Weibull distribution Gamma distribution Normal distribution |
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