mixest2 | R Documentation |
This function estimates recursively mixtures with normal regression components with a dynamic model of switching.
mixest2(y,x,mods=NULL,ftype=NULL,V=NULL,W=NULL,atype=NULL,Tvar=NULL)
y |
one column |
x |
|
mods |
see |
ftype |
optional, |
V |
optional, |
W |
optional, |
atype |
optional, |
Tvar |
optional, |
object of class mixest
, i.e., list
of
$y.hat |
|
$rvi |
|
$coef |
|
$weights |
|
$V |
|
$R |
|
$components |
|
$parameters |
|
Nagy, I., Suzdaleva, E., Karny, M., Mlynarova, T., 2011, Bayesian estimation of dynamic finite mixtures. International Journal of Adaptive Control and Signal Processing 25, 765–787.
Barbieri, M. M., Berger, J. O., 2004, Optimal predictive model selection. The Annals of Statistics 32, 870–897.
Burnham, K. P., Anderson, D. R., 2002, Model Selection and Multimodel Inference, Springer.
Dedecius, K., 2010, Partial Forgetting in Bayesian Estimation, Czech Technical University in Prague.
Karny, M. (ed.), 2006, Optimized Bayesian Dynamic Advising, Springer.
Nagy, I., 2015, Mixture Models and Their Applications, Czech Technical University in Prague.
Nagy, I., Suzdaleva, E., 2017, Algorithms and Programs of Dynamic Mixture Estimation, Springer.
Quarteroni, A., Sacco, R., Saleri, F., 2007, Numerical Mathematics, Springer.
mixest1
data(oil)
m1 <- mixest2(y=oil[,1,drop=FALSE],x=oil[,-1,drop=FALSE],ftype=1,V=100,W=100)
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