Abstract
Several methods have been used to estimate the unknown parameters in the two-parameter exponential distribution. Here we have considered two of these methods, maximum likelihood method and median-first order statistics method. However, in the presence of outliers these methods are not valid. In this paper we propose two approaches that deal with this situation. The idea is based on using first and third quartile instead of the minimum statistics. We investigated the parameters estimate using these methods through simulation study. The new method gives similar results under the normal situation and much better results when the data has outliers.
Highlights
The two-parameter exponential distribution is widely used in applied statistics since it has many applications in real life
Several methods have been used to estimate the unknown parameters in the two-parameter exponential distribution
Since the regular Maximum Likelihood Estimators (MLE) and the MOS chooses the minimum of the sample to estimate location parameter, this leads to a problem in estimating when the minimum of the sample is an outlier
Summary
The two-parameter exponential distribution is widely used in applied statistics since it has many applications in real life. It can be used to model the data such as the service times of agents in a system, the extreme values of annual snowfall or rainfall, the time it takes before your telephone call, the time until a radioactive particle decays, and the distance between mutations on a DNA strand. Estimation, predictions and inferential issues for the exponential distribution have been studied by several authors. In this paper we study the parameters estimate of the two-parameter exponential distribution in the presence of the outliers. Simulation studies are used to illustrate the accuracy of the proposed method estimates and to compare the method with other methods used to estimate the parameters of the exponential distribution with and without outliers in the data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Statistics and Probability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
