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Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation. (English) Zbl 1505.62267

Summary: 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.

MSC:

62-08 Computational methods for problems pertaining to statistics
62H10 Multivariate distribution of statistics
62H12 Estimation in multivariate analysis
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