ecmlsrobj

Log-likelihood function for least-squares regression with missing data

Syntax

Objective = ecmlsrobj(Data,Design,Parameters,Covariance)

Arguments

`Data`	`NUMSAMPLES`-by-`NUMSERIES` matrix with `NUMSAMPLES` samples of a `NUMSERIES`-dimensional random vector. Missing values are represented as `NaN`s. Only samples that are entirely `NaN`s are ignored. (To ignore samples with at least one `NaN`, use `mvnrmle`.)
`Design`	A matrix or a cell array that handles two model structures: If `NUMSERIES = 1`, `Design` is a `NUMSAMPLES`-by-`NUMPARAMS` matrix with known values. This structure is the standard form for regression on a single series. If `NUMSERIES` ≥ `1`, `Design` is a cell array. The cell array contains either one or `NUMSAMPLES` cells. Each cell contains a `NUMSERIES`-by-`NUMPARAMS` matrix of known values. If `Design` has a single cell, it is assumed to have the same `Design` matrix for each sample. If `Design` has more than one cell, each cell contains a `Design` matrix for each sample.
`Parameters`	`NUMPARAMS`-by-`1` column vector of estimates for the parameters of the regression model.
`Covariance`	(Optional) `NUMSERIES`-by-`NUMSERIES` matrix that contains a user-supplied estimate for the covariance matrix of the residuals of the regression. Default is an identity matrix.

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 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.

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