Description Usage Arguments Details Value Author(s) See Also Examples

Generate variance correction term of sample covariance of residuals described in Sun et al. (2016).

1 |

`data` |
tensor object stored in a m1 * m2 * ... * mK * n array, where n is sample size and mk is dimension of the kth tensor mode. |

`Omega.list` |
list of precision matrices of tensor, i.e., |

`k` |
index of interested mode, default is 1. |

This function computes variance correction term of sample covariance of residuals and is utilized to normalize test statistic into standord normal, see Sun et al. (2016).

A scalar of variance correction for the kth mode.

Will Wei Sun, Zhaoran Wang, Xiang Lyu, Han Liu, Guang Cheng.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
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
rho=covres(DATA, out.tlasso, k = k)
# sample covariance of residuals, including diagnoal
varpi2=varcor(DATA, out.tlasso, k = k)
# variance correction term for kth mode's sample covariance of residuals
``` |

Tlasso documentation built on May 29, 2017, 5:59 p.m.

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