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Showing 2 results for Ghoreishi
Kamran Ghoreishi,
Volume 8, Issue 2 (3-2015)
Abstract
In the Bayesian analysis of contingency tables, analysts commonly use special prior distributions for the parameters of log-linear models or the cell probabilities. But, in practice, sometimes there is some interpretive information which is rather on (generalized) odds ratios. So, it seems one will need a powerful approach so that he can model his prior believe on (generalized) odds ratios. Here, we refer to these priors as structural priors. In this paper we first introduce the general pattern of the structural priors. Then, since these priors have vast application in clinical trials and especially in the analysis of 2 x 2 complete and incomplete contingency tables, we obtain the corresponding structural priors, separately, under three conditions.
, Dr Seyed Kamran Ghoreishi,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, we first introduce semi-parametric heteroscedastic hierarchical models. Then, we define a new version of the empirical likelihood function (Restricted Joint Empirical likelihood) and use it to obtain the shrinkage estimators of the models' parameters in these models. Under different assumptions, a simulation study investigates the better performance of the restricted joint empirical likelihood function in the analysis of semi-parametric heterogeneity hierarchical models. Furthermore, we analyze an actual data set using the RJEL method.
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