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Recently, providing a new count regression model is very popular for many researchers. These count regression models are constructed by using a new discrete distribution or one of the existing distributions in the literature. In this paper, we consider a new zero-inflated regression model as an alternative to the zero-inflated regression models. We present two real data applications to illustrate the usefulness of the suggested regression model in modeling data, and compare the competitor models such as Poisson, discrete Lindley, and zero-inflated regression models. We provide a new count regression model which is useful in modeling overdispersed data. [ABSTRACT FROM AUTHOR]

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This article suggests a new statistical distribution named 'Cubic rank transmuted Kumaraswamy distribution' using cubic rank transmutation map. Various statistical properties of this new distribution such as, hazard function and its graphics, moments, variance, coefficients of skewness and kurtosis, moment generating function and order statistics are examined. In estimation problem, the maximum likelihood estimators of unknown parameters of this distribution are derived. A Monte Carlo simulation study based on bias and mean square error criteria of this estimator is conducted. Also, this new distribution is compared with other well known distributions in terms of fitting to the total milk production proportion, operation and empirical datasets in previous studies. [ABSTRACT FROM AUTHOR]