Description Usage Arguments Details Value Author(s) See Also Examples
Compute regression parameter of conditional linear model of separable tensor normal distribution described in Lyu et al. (2019).
1 | signal(Omega.list, i = 1, k = 1)
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Omega.list |
list of precision matrices of tensor, i.e., |
i |
index of interested regression parameter, default is 1. See details in Lyu et al. (2019). |
k |
index of interested mode, default is 1. |
This function computes regression parameter and is fundamental for sample covariance of residuals and bias correction. See details in Lyu et al. (2019).
A vector of regression paramter.
Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng.
1 2 3 4 5 6 7 8 9 10 11 12 | m.vec = c(5,5,5) # dimensionality of a tensor
n = 5 # sample size
k=1 # index of interested mode
lambda.thm = 20*c( sqrt(log(m.vec[1])/(n*prod(m.vec))),
sqrt(log(m.vec[2])/(n*prod(m.vec))),
sqrt(log(m.vec[3])/(n*prod(m.vec))))
DATA=Trnorm(n,m.vec,type='Chain')
# obersavations from tensor normal distribution
out.tlasso = Tlasso.fit(DATA,T=1,lambda.vec = lambda.thm)
# output is a list of estimation of precision matrices
signal(out.tlasso, i=2 , k=k )
# the regression parameter for conditional linear model of 2rd row in 1st mode
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