Log-likelihood function for multivariate normal regression with missing data
Objective = ecmmvnrobj(Data, Design, Parameters, Covariance,
NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. Missing values are represented as NaNs. Only samples that are entirely NaNs are ignored. (To ignore samples with at least one NaN, use mvnrmle.)
A matrix or a cell array that handles two model structures:
NUMPARAMS-by-1 column vector of estimates for the parameters of the regression model.
NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression.
(Optional) String that specifies the format for the covariance matrix. The choices are:
Objective = ecmmvnrobj(Data, Design, Parameters, Covariance, CovarFormat) computes a log-likelihood function based on current maximum likelihood parameter estimates with missing data. Objective is a scalar that contains the least-squares objective function.
You can configure Design as a matrix if NUMSERIES = 1 or as a cell array if NUMSERIES ≥ 1.
If Design is a cell array and NUMSERIES = 1, each cell contains a NUMPARAMS row vector.
If Design is a cell array and NUMSERIES > 1, each cell contains a NUMSERIES-by-NUMPARAMS matrix.