Loglikelihood function for leastsquares regression with missing data
Objective = ecmlsrobj(Data, Design, Parameters, Covariance)


 A matrix or a cell array that handles two model structures:



 (Optional) 
Objective = ecmlsrobj(Data, Design, Parameters, Covariance)
computes
a leastsquares objective function based on current parameter estimates
with missing data. Objective
is a scalar that contains
the leastsquares objective function.
ecmlsrobj
requires that Covariance
be
positivedefinite.
Note that
ecmlsrobj(Data, Design, Parameters) = ecmmvnrobj(Data, ... Design, Parameters, IdentityMatrix)
where IdentityMatrix
is a NUMSERIES
byNUMSERIES
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
byNUMPARAMS
matrix.
See Multivariate Normal Regression, LeastSquares Regression, CovarianceWeighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.