A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Annals of Mathematics and Artificial Intelligence
The meta-elliptical distributions with given marginals
Journal of Multivariate Analysis
Asymptotic efficiency of the two-stage estimation method for copula-based models
Journal of Multivariate Analysis
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Computational Statistics & Data Analysis
Efficient estimation of copula-GARCH models
Computational Statistics & Data Analysis
Tails of multivariate Archimedean copulas
Journal of Multivariate Analysis
Bayesian skew selection for multivariate models
Computational Statistics & Data Analysis
Modern Applied Statistics with S
Modern Applied Statistics with S
| Hi-index | 0.03 |
Recently an efficient fixed point algorithm, called maximization by parts (MBP), for finding maximum likelihood estimates has been applied to models based on Gaussian copulas. It requires a decomposition of a likelihood function into two parts and their iterative maximization by solving score equations. For the first time, the MBP algorithm is applied to multivariate meta t-distributions based on t-copulas. Since score equations for meta t-distributions do not have closed forms the proposed MBP algorithm in two variations maximizes the decomposed parts of the likelihood iteratively. Superiority of the proposed MBP algorithm over standard estimation methods such as inference for margins and direct maximization is illustrated in a simulation study. The usefulness of the proposed algorithm is shown in two data applications.