| Autor: |
Elgohari, Hanaa |
| Předmět: |
|
| Zdroj: |
International Journal of Agricultural & Statistical Sciences; Dec2019, Vol. 15 Issue 2, p475-479, 5p |
| Abstrakt: |
This article shows reparameterization technique that is used to improve the mixing of Markov Chain Monte Carlo (MCMC) algorithms. Moreover, one of the main reasons for MCMC method is to identify the correlation evidence between parameters of the model. Therefore, the main objective of the study is to estimate the correlations between a set of fixed effects and their variances. In addition, this study investigates to parameter expansion models including more than one random term, namely random slopes models. This technique will be applied on binary responses data where the sample size of 505 buffaloes collected from three farms. We conclude that the effective sample sizes for all parameters have been improved for this formulation while running time remains approximately the same. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
