Modelling strategies for value-added multilevel models are examined. These types of models typically include an endogenous variable and this causes difficulties for the standard estimation techniques that are commonly used to analyse multilevel models. Two alternative estimation strategies are proposed: one using an instrumental variable approach and the other using a Bayesian analysis as avail...

in this paper, a new cascaded multilevel inverter by capability of increasing the number of output voltage levels with reduced number of power switches is proposed. the proposed topology consists of series connection of a number of proposed basic multilevel units. in order to generate all voltage levels at the output, five different algorithms are proposed to determine the magnitude of dc volta...

The vast increase in computing power over recent decades has led to the emergence of multilevel models and its equivalents as practical and powerful analysis tools.1–6 This approach involves specifying two or more levels, or stages, of relationships among study variables and parameters. These levels are arranged in a hierarchy; hence the approach is also commonly known as hierarchical modelling...

A Bayesian representation of the analysis of variance by A. Gelman is introduced with ecological examples. These examples demonstrate typical situations encountered in ecological studies. Compared to conventional methods, the multilevel approach is more flexible in model formulation, easier to set up, and easier to present. Because the emphasis is on estimation, multilevel models are more infor...

BACKGROUND Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based ...

This paper suggests one method to process fMRI time series based on Bayesian inference for group analysis. The method is based on Bayesian inference to divide group into multilevel by session, subject and group levels. It compares covariance to select prior to reinforce posterior probability in group analysis. At the same time it combines classical statistics, i.e., t-statistics to obtain voxel...