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Showing 3 results for Kazemnejad
Behrooz Kavehie, Soghrat Faghihzadeh, Farzad Eskandari, Anooshiravan Kazemnejad, Tooba Ghazanfari, Volume 4, Issue 2 (3-2011)
Abstract
Sometimes it is impossible to directly measure the effect of intervention (medicine or therapeutic methods) in medical researches. That is because of high costs, long time, the aggressiveness of therapeutic methods, lack of clinical responses, and etc. In such cases, the effect of intervention on surrogate variables is measured. Many statistical studies have been accomplished for measuring the validity of surrogates and introducing a criterion for testing. The first criterion was established based on hypothesis testing. Other criterions were introduced over time. Then by using the classic methods, the Likelihood Ratio Factor was introduced. After that, the Bayesian Likelihood Ratio Factor developed and published. This article aims to introduce the Bayesian Likelihood Ratio Factor based on time dependent data. The illness under study is lung disease in victims of chemical weapons. The surrogate therapy method uses the forced expiratory volume at fist second.
Mohammad Gholami Fesharaki, Anoshirvan Kazemnejad, Farid Zayeri, Volume 6, Issue 1 (8-2012)
Abstract
Skew Normal distribution is important in analyzing non-normal data. The probability density function of skew Normal distribution contains integral function which tends researchers to some problems. Because of this problem, in this paper a simpler Bayesian approach using conditioning method is proposed to estimate the parameters of skew Normal distribution. Then the accuracy of this metrology is compared with ordinary Bayesian method in a simulation study.
Mohammad Gholami Fesharaki, Anoshirvan Kazemnejad, Farid Zayeri, Volume 7, Issue 2 (3-2014)
Abstract
In two level modeling, random effect and error's normality assumption is one of the basic assumptions. Violating this assumption leads to incorrect inference about coefficients of the model. In this paper, to resolve this problem, we use skew normal distribution instead of normal distribution for random and error components. Also, we show that ignoring positive (negative) skewness in the model causes overestimating (underestimating) in intercept estimation and underestimating (overestimating) in slope estimation by a simulation study. Finally, we use this model to study relationship between shift work and blood cholesterol.
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