Abstract
We introduce a multivariate version of the modified skew-normal distribution, which contains the multivariate normal distribution as a special case. Unlike the Azzalini multivariate skew-normal distribution, this new distribution has a nonsingular Fisher information matrix when the skewness parameters are all zero, and its profile log-likelihood of the skewness parameters is always a non-monotonic function. We study some basic properties of the proposed family of distributions and present an expectation-maximization (EM) algorithm for parameter estimation that we validate through simulation studies. Finally, we apply the proposed model to the univariate frontier data and to a trivariate wind speed data, and compare its performance with the Azzalini skew-normal model.
Original language | English (US) |
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Pages (from-to) | 511-555 |
Number of pages | 45 |
Journal | Statistical Papers |
Volume | 65 |
Issue number | 2 |
DOIs | |
State | Accepted/In press - 2023 |
Keywords
- EM algorithm
- Fisher information matrix
- Modified skew-normal
- Skew-elliptical
- Skew-generalized normal
- Skew-normal
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty