A Multivariate Flexible Skew-Symmetric-Normal Distribution: Scale-Shape Mixtures and Parameter Estimation via Selection Representation
| Autor: | Tsung-I Lin, Abbas Mahdavi, Vahid Amirzadeh, Ahad Jamalizadeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Hadamard product Physics and Astronomy (miscellaneous) Computer science Truncated normal distribution General Mathematics 01 natural sciences Data modeling Normal distribution 010104 statistics & probability 0502 economics and business Computer Science (miscellaneous) QA1-939 0101 mathematics ECME algorithm skew-symmetric distribution scale-shape mixtures 050205 econometrics Estimation theory 05 social sciences Probabilistic logic skew-contaminated-normal distribution Chemistry (miscellaneous) Skewness Kurtosis truncated normal distribution Algorithm Mathematics |
| Zdroj: | Symmetry, Vol 13, Iss 1343, p 1343 (2021) Symmetry Volume 13 Issue 8 |
| ISSN: | 2073-8994 |
| Popis: | Multivariate skew-symmetric-normal (MSSN) distributions have been recognized as an appealing tool for modeling data with non-normal features such as asymmetry and heavy tails, rendering them suitable for applications in diverse areas. We introduce a richer class of MSSN distributions based on a scale-shape mixture of (multivariate) flexible skew-symmetric normal distributions, called the SSMFSSN distributions. This very general class of SSMFSSN distributions can capture various shapes of multimodality, skewness, and leptokurtic behavior in the data. We investigate some of its probabilistic characterizations and distributional properties which are useful for further methodological developments. An efficient EM-type algorithm designed under the selection mechanism is advocated to compute the maximum likelihood (ML) estimates of parameters. Simulation studies as well as applications to a real dataset are employed to illustrate the usefulness of the presented methods. Numerical results show the superiority of our proposed model in comparison to several existing competitors. |
| Databáze: | OpenAIRE |
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