The synthetic control method (SCM) is a new, popular method developed for the purpose of estimating the effect of an intervention when only one single unit has been exposed. Other similar, unexposed units are combined into a synthetic control unit intended to mimic the evolution in the exposed unit, had it not been subject to exposure. As the inference relies on only a single observational unit...

Parameter ensembles or sets of random effects constitute one of the cornerstones of modern statistical practice. This is especially the case in Bayesian hierarchical models, where several decision theoretic frameworks can be deployed to optimise the estimation of parameter ensembles. The reporting of such ensembles in the form of sets of point estimates is an important concern in epidemiology, ...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator a model by applying principal component analysis to adjusted covariance matrix partially observed data. derive asymptotic distribution factors, loadings and imputed values under approximate general patterns. Th...

A great deal of recent research has focused on the theory and application of computationally intensive statistical techniques such as bootstrapping kernel density estimation Gibbs samplers etc Many of these techniques are especially well suited for complex models yet are still very underutilized by scienti c researchers Our interest is in the application of such Bayesian inferential methodology...