Name
Abstract | ||
|---|---|---|
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.neuroimage.2009.11.037 | NeuroImage |
Keywords | Field | DocType |
bayesian inference,fixed effects,random effects,time series,computer simulation,parameter estimation,causality,signal to noise ratio,magnetic resonance imaging,brain mapping | Population,Data set,Frequentist inference,Multivariate statistics,Statistics,Univariate,Mathematics,Bayesian probability,Causal model,Bayes' theorem | Journal |
Volume | Issue | ISSN |
49 | 4 | 1053-8119 |
Citations | PageRank | References |
8 | 0.62 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Herbert Kasess | 1 | 8 | 0.62 |
| Klaas Enno Stephan | 2 | 716 | 36.53 |
| Andreas Weissenbacher | 3 | 155 | 10.30 |
| Lukas Pezawas | 4 | 54 | 4.10 |
| Ewald Moser | 5 | 343 | 31.55 |
| Christian Windischberger | 6 | 381 | 37.71 |