A scale-shape mixtures of flexible generalized skew normal (SSMFGSN) distributions is proposed as a novel device for modeling asymmetric data. Computationally feasible EM-type algorithms derived from the selection mechanism are presented to compute maximum likelihood (ML) estimates of SSMFGSN distributions. Some characterizations and probabilistic properties of the SSMFGSN distributions are also studied. Monte Carlo simulations show that the proposed estimating procedures can provide desirable asymptotic properties of the ML estimates and demand less computational burden in comparison with other existing algorithms based on convolution representations. The usefulness of the proposed methodology is illustrated by analyzing a real dataset.

提出了一种灵活的广义偏斜正态 (SSMFGSN) 分布的尺度混合，作为一种新的非对称数据建模设备。提出了从选择机制派生的计算上可行的 EM 类型算法来计算 SSMFGSN 分布的最大似然 (ML) 估计。还研究了 SSMFGSN 分布的一些特征和概率特性。蒙特卡罗模拟表明，与其他基于卷积表示的现有算法相比，所提出的估计程序可以提供理想的 ML 估计的渐近特性，并且需要更少的计算负担。通过分析真实数据集说明了所提出方法的有用性。