In this paper, the estimation of the unknown parameters of the kumaraswamy distribution is considered using both simple random sampling (SRS) and ranked set sampling (RSS) techniques. The estimation is based on maximum likelihood estimation and Bayesian estimation methods. A simulation study is made to compare the resultant estimators in terms of their biases and mean square errors. The efficie...

We use a two-parameter family of bounded distribution functions (Kumaraswamy) to fit electronic textbook (etext) usage in 20 blended and online courses from Michigan State University, MIT, and edX. We observe clusters of courses in the parameter space that correlate with course structural features such as frequency of exams.

We consider the AR(1) time series model Xt − βXt−1 = ξt, β−p ∈ N {1}, when Xt has Beta distribution B(p, q), p ∈ (0, 1], q > 1. Special attention is given to the case p = 1 when the marginal distribution is approximated by the power law distribution closely connected with the Kumaraswamy distribution Kum(p, q), p ∈ (0, 1], q > 1. Using the Laplace transform technique, we prove that for p = 1 th...

Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy Marshall-Olkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some importan...

Abstract: The linear exponential distribution is a very well-known distribution for modeling lifetime data in reliability and medical studies. We introduce in this paper a new four-parameter generalized version of the linear exponential distribution which is called Kumaraswamy linear exponential distribution. We provide a comprehensive account of the mathematical properties of the new distribut...