Log-likelihood function for least-squares regression with missing data
Objective = ecmlsrobj(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.
(Optional) NUMSERIES-by-NUMSERIES matrix that contains a user-supplied estimate for the covariance matrix of the residuals of the regression. Default is an identity matrix.
Objective = ecmlsrobj(Data, Design, Parameters, Covariance) computes a least-squares objective function based on current parameter estimates with missing data. Objective is a scalar that contains the least-squares objective function.
ecmlsrobj requires that Covariance be positive-definite.
ecmlsrobj(Data, Design, Parameters) = ecmmvnrobj(Data, ... Design, Parameters, IdentityMatrix)
where IdentityMatrix is a NUMSERIES-by-NUMSERIES identity matrix.
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.