View source: R/CP_functions_unified.R
DGP.CP | R Documentation |
DGP.CP()
function generate the matrix time series described in Chang et al. (2023):
{\bf{Y}}_t = {\bf A \bf X}_t{\bf B}^{'} +
{\boldsymbol{\epsilon}}_t,
where {\bf X}_t = diag(x_{t,1},\ldots,x_{t,d})
is an d \times d
latent process, {\bf A}
and {\bf B}
are , respectively, p
\times d
and q \times d
unknown constant matrix, and {\boldsymbol{\epsilon}}_t
is a p \times q
matrix white noise process.
DGP.CP(n, p, q, d1, d2, d)
n |
Integer. Sample size of |
p |
Integer. Number of rows of |
q |
Integer. Number of columns of |
d1 |
Integer. Rank of |
d2 |
Integer. Rank of |
d |
Integer. Number of columns of |
A list containing the following components:
Y |
A |
S |
A |
A |
A |
B |
A |
X |
A |
P |
A |
Q |
A |
U |
A |
V |
A |
W |
A |
Ws |
A |
Xmat |
A |
Smat |
A |
Chang, J., He, J., Yang, L. and Yao, Q.(2023). Modelling matrix time series via a tensor CP-decomposition. Journal of the Royal Statistical Society Series B: Statistical Methodology, Vol. 85(1), pp.127–148.
CP_MTS
.
p = 10
q = 10
n = 400
d = d1 = d2 = 3
data <- DGP.CP(n,p,q,d1,d2,d)
Y = data$Y
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