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ecmmvnrobj

Log-likelihood function for multivariate normal regression with missing data

Syntax

Objective = ecmmvnrobj(Data,Design,Parameters,Covariance,CovarFormat)

Arguments

Data

NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. Missing values are represented as NaNs. Only samples that are entirely NaNs 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

NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression.

CovarFormat

(Optional) Character vector that specifies the format for the covariance matrix. The choices are:

  • 'full' — Default method. The covariance matrix is a full matrix.

  • 'diagonal' — The covariance matrix is a diagonal matrix.

Description

Objective = ecmmvnrobj(Data,Design,Parameters,Covariance,CovarFormat) computes a log-likelihood function based on current maximum likelihood parameter estimates with missing data. Objective is a scalar that contains the least-squares objective function.

Notes

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.

Introduced in R2006a

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