TY - JOUR
AU - Nesselroade, John R.
AB -  Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations—the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model—to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with a categorical time series data set from an emotion study. To estimate the categorical dynamic factor models, a Bayesian method via Gibbs sampling is used. The results show that today's affect directly influences tomorrow's affect. The results are then validated by means of simulation studies. Differences between continuous and categorical dynamic factor models are examined. 
TI - Bayesian Estimation of Categorical Dynamic Factor Models
JF - Multivariate Behavioral Research
DO - 10.1080/00273170701715998
DA - 2007-12-28
UR - https://www.deepdyve.com/lp/taylor-francis/bayesian-estimation-of-categorical-dynamic-factor-models-3OCsgbI1yA
SP - 729
EP - 756
VL - 42
IS - 4
DP - DeepDyve
ER -