Negative log-likelihood for multivariate regression

`nlogL = mvregresslike(X,Y,b,SIGMA,`

* alg*)

[nlogL,COVB] = mvregresslike(...)

[nlogL,COVB] = mvregresslike(...,

`type`

`format`

`nlogL = mvregresslike(X,Y,b,SIGMA,`

computes
the negative log-likelihood * alg*)

`nlogL`

for a multivariate
regression of the `Y`

on
the predictor variables in the matrix or cell array `X`

,
evaluated for the `b`

of
coefficient estimates and the `SIGMA`

specifying
the covariance of a row of `Y`

. If `X`

can be an `X`

can
also be a cell array of length `X`

can be a single cell.`NaN`

values in `X`

or `Y`

are
taken as missing. Observations with missing values in `X`

are
ignored. Treatment of missing values in `Y`

depends
on the algorithm specified by * alg*.

* alg* should match the algorithm used
by

`mvregress`

to obtain the coefficient
estimates `b`

, and must be one of the following:`'ecm'`

— ECM algorithm`'cwls'`

— Least squares conditionally weighted by`SIGMA`

`'mvn'`

— Multivariate normal estimates computed after omitting rows with any missing values in`Y`

`[nlogL,COVB] = mvregresslike(...)`

also
returns an estimated covariance matrix `COVB`

of
the parameter estimates `b`

.

`[nlogL,COVB] = mvregresslike(...,`

specifies
the type and format of * type*,

`format`

`COVB`

.* type* is either:

`'hessian'`

— To use the Hessian or observed information. This method takes into account the increased uncertainties due to missing data. This is the default.`'fisher'`

— To use the Fisher or expected information. This method uses the complete data expected information, and does not include uncertainty due to missing data.

* format* is either:

`'beta'`

— To compute`COVB`

for`b`

only. This is the default.`'full'`

— To compute`COVB`

for both`b`

and`SIGMA`

.

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