کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
415933 | 681263 | 2011 | 19 صفحه PDF | دانلود رایگان |

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 tt-distributions based on tt-copulas. Since score equations for meta tt-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.
Journal: Computational Statistics & Data Analysis - Volume 55, Issue 3, 1 March 2011, Pages 1196–1214