Investigation of Beta Inverse Weibull Distribution
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Document Type
Event
Description
The Inverse Weibull distribution is one of the widely-applied distributions. In this paper, we are going to extend the study of the Beta Inverse Weibull Distribution (BIW). We are going to investigate the parameter estimation of BIW distribution. Determined by three parameters, the BIW distribution has a relatively complicated form, which causes difficulties for parameter estimation. The Maximum Likelihood Estimation (MLE) procedure is implemented in the BIW distribution with three parameters. In computation, the Newton-Raphson method is applied using single and multiple initializations, the steepest descent method is also implemented using single and multiple initializations, and the simulation results are given. Finally, practical use of the methods is demonstrated by using an application to real data.
Keywords
beta function, maximum likelihood estimation, Newton-Raphson method, steepest descent method
Degree
Master of Science (MS)
Department
Mathematics and Statistics
College
Science, Engineering and Technology
First Faculty Advisor's Name
Mezbahur Rahman
First Faculty Advisor's Department
Mathematics and Statistics
First Faculty Advisor's College
Science, Engineering and Technology
Recommended Citation
Islam, M.N. (2017, April 17). Investigation of Beta Inverse Weibull Distribution. Presented at the 2017 Graduate Online Symposium, Mankato, MN. http://cornerstone.lib.mnsu.edu/gos/2017/presentation/4
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Presentation Handout
Investigation of Beta Inverse Weibull Distribution
The Inverse Weibull distribution is one of the widely-applied distributions. In this paper, we are going to extend the study of the Beta Inverse Weibull Distribution (BIW). We are going to investigate the parameter estimation of BIW distribution. Determined by three parameters, the BIW distribution has a relatively complicated form, which causes difficulties for parameter estimation. The Maximum Likelihood Estimation (MLE) procedure is implemented in the BIW distribution with three parameters. In computation, the Newton-Raphson method is applied using single and multiple initializations, the steepest descent method is also implemented using single and multiple initializations, and the simulation results are given. Finally, practical use of the methods is demonstrated by using an application to real data.