mle3d_svc: Maximum likelihood estimation of the parameters of a...

In sEparaTe: Maximum Likelihood Estimation and Likelihood Ratio Test Functions for Separable Variance-Covariance Structures

Maximum likelihood estimation of the parameters of a 3rd-order tensor normal distribution

Description

Maximum likelihood estimation for the parameters of a 3rd-order tensor normal distribution X, which is characterized by a doubly separable variance-covariance structure. In the general case, which is the case considered here, three unstructured factor variance-covariance matrices determine the covariability of random tensor entries, depending on the row (one factor matrix), the column (another factor matrix) and the edge (remaining factor matrix) where two X-entries are. In the required function, the Id3, Id4 and Id5 variables correspond to the row, column and edge subscripts, respectively; “value3d” indicates the observed variable.

Usage

mle3d_svc(
  value3d,
  Id3,
  Id4,
  Id5,
  subject,
  data_3d,
  eps,
  maxiter,
  startmatU2,
  startmatU3
)

Arguments

value3d	from the formula value3d ~ Id3 + Id4 + Id5
Id3	from the formula value3d ~ Id3 + Id4 + Id5
Id4	from the formula value3d ~ Id3 + Id4 + Id5
Id5	from the formula value3d ~ Id3 + Id4 + Id5
subject	the replicate, also called individual
data_3d	the name of the tensor data
eps	the threshold in the stopping criterion for the iterative mle algorithm
maxiter	the maximum number of iterations for the iterative mle algorithm
startmatU2	the value of the second factor variance covariance matrix used for initialization
startmatU3	the value of the third factor variance covariance matrix used for initialization, i.e., startmatU3 together with startmatU2 are used to start the algorithm and obtain the initial estimate of the first factor variance covariance matrix U1

Output

“Convergence”, TRUE or FALSE

“Iter”, the number of iterations needed for the mle algorithm to converge

“Xmeanhat”, the estimated mean tensor (i.e., the sample mean)

“First”, the row subscript, or the second column in the data file

“U1hat”, the estimated variance-covariance matrix for the rows

“Standardized.U1hat”, the standardized estimated variance-covariance matrix for the rows; the standardization is performed by dividing each entry of U1hat by entry(1, 1) of U1hat

“Second”, the column subscript, or the third column in the data file

“U2hat”, the estimated variance-covariance matrix for the columns

“Standardized.U2hat”, the standardized estimated variance-covariance matrix for the columns; the standardization is performed by multiplying each entry of U2hat by entry(1, 1) of U1hat

“Third”, the edge subscript, or the fourth column in the data file

“U3hat”, the estimated variance-covariance matrix for the edges

“Shat”, the sample variance-covariance matrix computed from the vectorized data tensors

Reference

Manceur AM, Dutilleul P. 2013. Maximum likelihood estimation for the tensor normal distribution: Algorithm, minimum sample size, and empirical bias and dispersion. Journal of Computational and Applied Mathematics 239: 37-49.

Examples

output <- mle3d_svc(data3d$value3d, data3d$Id3, data3d$Id4, data3d$Id5, data3d$K, data_3d = data3d)
output

sEparaTe documentation built on Aug. 18, 2023, 9:07 a.m.