Description Usage Arguments Value Author(s) References See Also Examples
The concentrated loglikelihood, that is, the loglikelihood function maximized over the innovation variance parameter, is computed.
1 | LLFGN(H, z)
|
H |
parameter |
z |
data vector, assumed to be mean corrected |
the value of the loglikelihood
A.I. McLeod
McLeod, A.I., Yu, Hao, Krougly, Zinovi L. (2007). Algorithms for Linear Time Series Analysis, Journal of Statistical Software.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #compute loglikelihood for NileFlowCMS with H=0.9
data(NileFlowCMS)
z<-NileFlowCMS
z<-z-mean(z)
LLFGN(0.9, z)
#simulate Gaussian white noise and tabulate the loglikelihood for H=0.40, 0.45, 0.50, 0.55, 0.60
set.seed(4321)
h<-c(0.40, 0.45, 0.50, 0.55, 0.60)
z<-rnorm(500, 100, 50)
z<-z-mean(z)
LL<-numeric(length(h))
for (i in 1:length(h))
LL[i]<-LLFGN(h[i],z)
matrix(c(h,LL),ncol=2)
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