Cordeiro and de Castro [A new family of generalized distributions. Journal of Statistical Computation and Simulation, 81, 2011, 883-898] proposed a new family of generalized distributions based on the Kumaraswamy distribution (denoted Kw-G). Nadarajah et al. [General results for the Kumaraswamy-G distribution. Journal of Statistical Computation and Simulation, 82, 2012, 951-979] showed that the density function of the new family of distributions can be expressed as a linear combination of the density of exponentiated family of distributions. They derived some properties of Kw-G distributions and discussed estimation of parameters using the maximum likelihood (ML) method. Cheng and Amin [Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society, B, 45, 1983, 394-403] and Ranneby [The maximum spacing method. An estimation method related to the maximum likelihood method. Scandinavian Journal of Statistics, 11, 1984, 93-112] introduced a new method of estimating parameters based on Kullback-Leibler divergence (the maximum spacing (MSP) method). In this paper, the estimates of parameters of Kw-G distributions are obtained using the MSP method. For some special Kw-G distributions the new estimators are compared with ML estimators. It is 1 ACCEPTED MANUSCRIPT Downloaded by [Selcuk Universitesi] at 07:24 05 January 2015
ACCEPTED MANUSCRIPTshown by simulations and a real data application that MSP estimators have better properties than ML estimators.
Kharazmi and Saadatinik [21] introduced a new family of distribution called hyperbolic cosine-F (HCF) distributions. They studied some properties of this model and obtained the estimates of its parameters by different methods. In this paper, it is focused on a special case of HCF family with Weibull distribution as a baseline model. Various properties of the proposed distribution including explicit expressions for the moments, quantiles, moment generating function, failure rate function, mean residual lifetime, order statistics and expression of the entropies are derived. Superiority of this model is proved in some simulations and applications.
In the present paper, we study the information generating (IG) function and relative information generating (RIG) function measures associated with maximum and minimum ranked set sampling (RSS) schemes with unequal sizes. We also examine the IG measures for simple random sampling (SRS) and provide some comparison results between SRS and RSS procedures in terms of dispersive stochastic ordering. Finally, we discuss the RIG divergence measure between SRS and RSS frameworks.
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