| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Campinas, Dept Estat, Caixa Postal 6065, Sao Paulo - Brazil
[2] McMaster Univ, Dept Math & Stat, Hamilton, ON - Canada
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | STATISTICS; v. 52, n. 2, p. 321-344, 2018. |
| Web of Science Citations: | 2 |
| Abstract | |
We propose here a robust multivariate extension of the bivariate Birnbaum-Saunders (BS) distribution derived by Kundu et al. {[}Bivariate Birnbaum-Saunders distribution and associated inference. J Multivariate Anal. 2010;101:113-125], based on scale mixtures of normal (SMN) distributions that are used for modelling symmetric data. This resulting multivariate BS-type distribution is an absolutely continuous distribution whose marginal and conditional distributions are of BS-type distribution of Balakrishnan et al. {[}Estimation in the Birnbaum-Saunders distribution based on scalemixture of normals and the EM algorithm. Stat Oper Res Trans. 2009;33:171-192]. Due to the complexity of the likelihood function, parameter estimation by direct maximization is very difficult to achieve. For this reason, we exploit the nice hierarchical representation of the proposed distribution to propose a fast and accurate EM algorithm for computing the maximum likelihood (ML) estimates of the model parameters. We then evaluate the finite-sample performance of the developed EM algorithm and the asymptotic properties of the ML estimates through empirical experiments. Finally, we illustrate the obtained results with a real data and display the robustness feature of the estimation procedure developed here. (AU) | |
| FAPESP's process: | 13/25935-2 - Bivariate regression model Birnbaum-Saunders |
| Grantee: | Renata Guimarães Romeiro Agostinho |
| Support Opportunities: | Scholarships in Brazil - Doctorate |