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Showing 2 results for Eshaghi
Ehsan Eshaghi, Hossein Baghishani, Davood Shahsavani,
Volume 7, Issue 1 (9-2013)
Abstract
In some semiparametric survival models with time dependent coefficients, a closed-form solution for coefficients estimates does not exist. Therefore, they have to be estimated by using approximate numerical methods. Due to the complicated forms of such estimators, it is too hard to extract their sampling distributions. In such cases, one usually uses the asymptotic theory to evaluate properties of the estimators. In this paper, first the model is introduced and a method is proposed, by using the Taylor expansion and kernel methods, to estimate the model. Then, the consistency and asymptotic normality of the estimators are established. The performance of the model and estimating procedure are evaluated by a heavy simulation study as well. Finally, the proposed model is applied on a real data set on heart disease patients in one of the Mashhad hospitals.
Sedighe Eshaghi, Hossein Baghishani, Negar Eghbal,
Volume 12, Issue 1 (9-2018)
Abstract
Introducing some efficient model selection criteria for mixed models is a substantial challenge; Its source is indeed fitting the model and computing the maximum likelihood estimates of the parameters. Data cloning is a new method to fit mixed models efficiently in a likelihood-based approach. This method has been popular recently and avoids the main problems of other likelihood-based methods in mixed models. A disadvantage of data cloning is its inability of computing the maximum of likelihood function of the model. This value is a key quantity in proposing and calculating information criteria. Therefore, it seems that we can not, directly, define an appropriate information criterion by data cloning approach. In this paper, this believe is broken and a criterion based on data cloning is introduced. The performance of the proposed model selection criterion is also evaluated by a simulation study.
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