ecmlsrobj

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

Syntax

Objective = ecmlsrobj(Data,Design,Parameters)

Objective = ecmlsrobj(___,Covariance)

Description

Objective = ecmlsrobj(Data,Design,Parameters) 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.

Objective = ecmlsrobj(___,Covariance) computes a least-squares objective function based on current parameter estimates with missing data using an optional argument.

Input Arguments

Data — Data sample

matrix

Data sample, specified as an NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. If a data sample has missing values, represented as NaNs. Only samples that are entirely NaNs are ignored. (To ignore samples with at least one NaN, use mvnrmle.)

Data Types: double

Design — Model design

matrix | cell array of character vectors

Model design, specified as 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.

Data Types: double | cell

Parameters — Estimates for the parameters of regression model

vector

Estimates for the parameters of regression model, specified as an NUMPARAMS-by-1 column vector.

Data Types: double

Covariance — User-supplied estimate for covariance matrix of residuals of the regression

vector

(Optional) User-supplied estimate for the covariance matrix of the residuals of the regression, specified as an NUMPARAMS-by-1 column vector.

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.

Data Types: double

Output Arguments

Objective — Least-squares objective function

scalar

Least-squares objective function, returned as scalar.

Version History

Introduced in R2006a