Abstract
In this study, we construct a mixture of bivariate inverse Weibull distribution. We assumed that the parameters of two marginals have Bernoulli distributions. Several properties of the proposed model are obtained, such as probability marginal density function, probability marginal cumulative function, the product moment, the moment of the two variables x and y, the joint moment-generating function, and the correlation between x and y. The real dataset has been analyzed. We observed that the mixture bivariate inverse Weibull distribution provides a better fit than the other model.
Highlights
In the history of statistics, the use of finite mixture models is very old
We compare the fits of the new bivariate inverse Weibull mixture with the other competitive models, such as BIWM, bivariate gamma mixture (MBG), mixture inverse Weibull (MIW), and bivariate inverse Weibull (BIW) distributions. e comparison is done based on some measures of goodness of fit, the maximized loglikelihood under the (− l), Akaike information criterion (AIC), Bayesian information criterion (BIC), consistent Akaike information criterion (CAIC), Hannan–Quinn information criterion (HQIC), and Kolmogorov–Smirnov (KS) statistic and its P value (PV)
We suggest BIWM distribution as a new finite mixture bivariate model
Summary
In the history of statistics, the use of finite mixture models is very old. It was used to model population heterogeneity and generalize distributional assumptions, clustering and classification, and so on. Mendenhall and Hader [2] considered exponentially distributed failure time distributions based on censored lifetime data to estimate the model parameters using the maximum likelihood method. In their study, they divided the failure population into two subpopulations, each representing a different cause or type of failure. Sultan and Moisheer [19] found the maximum likelihood estimates of the parameters of the mixture of two inverse Weibull distributions by using classified and unclassified observations.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: 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.
