ecmlsrobj
Log-likelihood function for least-squares regression with missing data
Syntax
Objective = ecmlsrobj(Data,Design,Parameters,Covariance)
Arguments
|
|
| A matrix or a cell array that handles two model structures:
|
|
|
| (Optional) |
Description
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.
Notes
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 andNUMSERIES
=1
, each cell contains aNUMPARAMS
row vector.If
Design
is a cell array andNUMSERIES
>1
, each cell contains aNUMSERIES
-by-NUMPARAMS
matrix.
Examples
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.
Version History
Introduced in R2006a