The Sine Kumaraswamy-G Family of Distributions

In this paper, we introduce a new trigonometric family of continuous distributions called the sine Kumaraswamy-G family of distributions. It can be presented as a natural extension of the well- established sine-G family of distributions, with new perspectives in terms of applicability. We investigate the main mathematical properties of the sine Kumaraswamy-G family of distributions, including asymp- totes, quantile function, linear representations of the cumulative distri- bution and probability density functions, moments, skewness, kurtosis, incomplete moments, probability weighted moments and order statis- tics. Then, we focus our attention on a special member of this family called the sine Kumaraswamy exponential distribution. The statisti- cal inference for the related parametric model is explored by using the maximum likelihood method. Among others, asymptotic condence in- tervals and likelihood ratio tests for the parameters are discussed. A simulation study is performed under varying sample sizes to assess the performance of the model. Finally, applications to two practical data sets are presented to illustrate its potentiality and robustness.