@Article{csse.2021.014270,
AUTHOR = {Yen Liang Tung, Zubair Ahmad, Eisa Mahmoudi},
TITLE = {The <i>Arcsine-X</i> Family of Distributions with Applications to Financial Sciences},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {39},
YEAR = {2021},
NUMBER = {3},
PAGES = {351--363},
URL = {http://www.techscience.com/csse/v39n3/44047},
ISSN = {},
ABSTRACT = {The heavy-tailed distributions are very useful and play a major role in actuary and financial management problems. Actuaries are often searching for such distributions to provide the best fit to financial and economic data sets. In the current study, a prominent method to generate new distributions useful for modeling heavy-tailed data is considered. The proposed family is introduced using trigonometric function and can be named as the <i>Arcsine-X</i> family of distributions. For the purposes of the demonstration, a specific sub-model of the proposed family, called the <i>Arcsine</i>-Weibull distribution is considered. The maximum likelihood estimation method is adopted for estimating the parameters of the <i>Arcsine-X</i> distributions. The resultant estimators are evaluated in a detailed Monte Carlo simulation study. To illustrate the <i>Arcsine</i>-Weibull two insurance data sets are analyzed. Comparison of the <i>Arcsine</i>-Weibull model is done with the well-known two parameters and four parameters competitors. The competitive models including the Weibull, Lomax, Burr-XII and beta Weibull models. Different goodness of fit measures are taken into account to determine the usefulness of the Arcsine-Weibull and other considered models. Data analysis shows that the <i>Arcsine</i>-Weibull distribution works much better than competing models in financial data analysis.},
DOI = {10.32604/csse.2021.014270}
}