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.

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.

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.