Log-likelihood function for least-squares regression with missing data
Objective = ecmlsrobj(Data,Design,Parameters,Covariance)
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| A matrix or a cell array that handles two model structures:
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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.
Note that
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.
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.