## 2017 Graduate Online Symposium

### Investigation of Beta Inverse Weibull Distribution

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

Mezbahur Rahman

#### First Faculty Advisor's Department

Mathematics and Statistics

#### First Faculty Advisor's College

Science, Engineering and Technology

#### Creative Commons License

Islam_Md_Nur_Handout.pdf (347 kB)
Presentation Handout

#### Share

COinS

Apr 17th, 12:00 AM Apr 17th, 12:00 AM

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.