Abstract
We introduce and study the Kumaraswamy Lindely Distribution (KLD) model, which has increasing, decreasing, upside-down bathtub and bathtub shaped hazard functions.. We perform a Monte Carlo simulation study to assess the finite sample behavior of the maximum likelihood estimates of the parameters. We define a new regression model based on the new distribution. The new regression was applied to data from the Egyptian stock exchange in the period of (2015-2019). Finally, we study some properties of regression Residual analysis The martingale residual, Deviance component residual.
Highlights
There are many researchers who have been exposed to the Kumaraswamy Lindley distribution to the proposed Kumrsawamy Lindely distribution (KLD)
Et al [5] devoted a generalized class of Kumaraswamy Lindley distribution with applications to lifetime data
We suggest appropriate link function to make a regression model that contributes with the KLD
Summary
There are many researchers who have been exposed to the Kumaraswamy Lindley distribution to the proposed Kumrsawamy Lindely distribution (KLD). Et al [10] presented a new generalized Lindley distribution. Et al [9] presented the Kumaraswamy normal linear regression model with applications. Cakmakyapan, et al [7] presented the Kumaraswamy Marshall-Olkin log-logistic distribution with application. Et al [22] presented the Poisson-Weibull regression model. Et al [17] presented the transmuted Geometric-Weibull distribution and its regression model. Et al [11] presented exponentiated Kumaraswamy-Weibull distribution with application to real data. Fachini-Gomes, et al [12] presented the Bivariate Kumaraswamy Weibull regression model. Et al [16] presented Kumaraswamy log-logistic Weibull distribution, model theory and application to. This paper presents a new regression model.
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