Log-likelihood function for multivariate normal regression without missing data
Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)
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| A matrix or a cell array that handles two model structures:
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| (Optional) Character vector that specifies the format for the covariance matrix. The choices are:
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Objective = mvnrobj(Data,Design,Parameters,Covariance,CovarFormat)
computes
the log-likelihood function based on current maximum likelihood parameter
estimates without missing data. Objective
is a
scalar that contains the log-likelihood 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.
Although Design
should not have NaN
values,
ignored samples due to NaN
values in Data
are
also ignored in the corresponding Design
array.
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.