observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling …

subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …

observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling …

observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling …

. The subsample is taken via the cube method, a balanced sampling design, which is defined by the property that the sample … dass dieser nicht gespeichert werden muss. Die Stichprobe wird via cube sampling, einem balanciertem Stichprobendesign …

This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the … parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids … the tedious task of tuning a MCMC sampling algorithm. The usage of the package is shown in an empirical application to …

subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling …

importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … robust and flexible estimation strategy is demonstrated where the complete posterior distribution is explored. In this … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …