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We propose scale mixtures of Birnbaum-Saunders distributions as a new class of positive skewed and leptokurtic distributions and use it to model volatility in stock markets. To estimate the model parameters, we develop an Expectation-Conditional-Maximization algorithm. The numerical performance of the proposed methodology is evaluated by means of Monte Carlo simulations. Application of the new model in volatility modeling is illustrated with some real-life data. [ABSTRACT FROM AUTHOR]

This paper introduces the shape mixtures of the skew scale mixtures of normal distribution which are contained additional shape parameters to regulate skewness and kurtosis. We present a finite mixture model for this new family of distributions, which is a novel model-based tool for clustering heterogeneous data in the presence of skewed and heavy-tailed outcomes. The maximum likelihood estimates of the parameters of the proposed models are obtained by developing an Expectation Conditional Maximization Either algorithm. The numerical performance of the proposed methodology is illustrated through simulated and real data examples. [ABSTRACT FROM AUTHOR]

This paper presents a skew-normal mean-variance mixture based on Birnbaum-Saunders (SNMVBS) distribution and discusses some of its key properties. The SNMVBS distribution can be thought as a flexible extension of the normal mean-variance mixture based on Birnbaum-Saunders (NMVBS) distribution as it possesses one additional shape parameter for providing more flexibility with skewness and kurtosis. Next, we develop a computationally feasible ECM algorithm for the maximum likelihood estimation of the model parameters. Asymptotic standard errors of the ML estimates are obtained through an approximation of the observed information matrix. Finally, the usefulness of the proposed model and its fitting method are illustrated through a Monte-Carlo simulation as well as three real-life datasets. [ABSTRACT FROM AUTHOR]

Birnbaum-Saunders (BS) distribution is a model with positive domain that is used in many fields including reliability and environmental studies. This article introduces a generalized version of the BS distribution which arises from the shape mixture of skew-normal distribution. A feasible EM type algorithm is developed to obtain maximum likelihood (ML) estimates of pa-rameters of the new model. The asymptotic standard errors of ML estimates are obtained via the information-based approximation. The robustness and application of the proposed methodology are illustrated through simulation studies and air pollution analysis. [ABSTRACT FROM AUTHOR]

Cordeiro and de Castro proposed a new family of generalized distributions based on the Kumaraswamy distribution (denoted as Kw-G). Nadarajah et al. 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 and Ranneby introduced a new method of estimating parameters based on Kullback-Leibler divergence (the maximum spacing (MSP) method). In this article, 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 shown by simulations and a real data application that MSP estimators have better properties than ML estimators. [ABSTRACT FROM AUTHOR]