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In recent years, multilevel regression models were intensely developed in many fields like medicine, psychology economic and the others. Such models are applicable for hierarchical data that micro levels are nested in macros. For modeling these data, when response is not normality distributed, we use generalized multilevel regression models. In this paper, at first, multilevel ordinal logistic regression models and some estimation methods are explained. So their applications are investigated in the effect of patient’s environment on economic burden of diabetes type 2.

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Due to the economical restrictions, improving efficiency in the collection and processing of blood products at blood centers is important. This study uses data envelopment analysis (DEA) to evaluate the efficiency of 31 Provincial units of Iranian Blood Transfusion Organizations (IBTO) to determine to what extent efficiency can be improved. Efficiency grades were computed with DEA linear programming techniques and Provincial units of IBTO characteristics that important affect efficiency determined for two consecutive years, 22 Provincial units of IBTO were efficient and 9 were inefficient. Otherwise based on PCA results, Efficiency was mainly affected by Number of BCPCs, BCCs, MTs, Efficiency did not directly relate to Population density of province, Number of donor beds and area of BTC.  The major reason of inefficiency was excess allocating resulting from a suboptimal combination of Number of BCPCs, BCCs, and MTs.

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Varying coefficient models are among the most important tools for discovering the dynamic patterns when a fixed pattern does not fit adequately well on the data, due to existing diverse temporal or local patterns. These models are natural extensions of classical parametric models that have achieved great popularity in data analysis with good interpretability. The high flexibility and interpretability of these models have led to use in many real applications. In this paper, after presenting a brief review of varying coefficient models, we use the parameter estimation method using the kernel function and cubic
spline then confidence band and hypothesis testing are investigated. Finally, using the real data of Iran’s inflation rate from 1989 to 2017, we show the application and capabilities of the varying coefficient model in interpreting the results. The main challenge in this application is that the panel or longitudinal models or even time series models with heterogeneous variances such as ARCH and GARCH models and their derived models did not fit adequately well on this dataset which justifies the use of varying coefficient models.