Log-likelihood function for multivariate normal regression with missing data
Data — Data sample
Data sample, specified as an
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
Design — Model design
matrix | cell array of character vectors
Model design, specified as a matrix or a cell array that handles two model structures:
NUMSERIES = 1,
NUMPARAMSmatrix with known values. This structure is the standard form for regression on a single series.
Designis a cell array. The cell array contains either one or
NUMSAMPLEScells. Each cell contains a
NUMPARAMSmatrix of known values.
Designhas a single cell, it is assumed to have the same
Designmatrix for each sample. If
Designhas more than one cell, each cell contains a
Designmatrix for each sample.
Parameters — Estimates for the parameters of regression model
Estimates for the parameters of regression model, specified as an
Covariance — Estimates for covariance matrix of residuals of the regression
Estimates for the covariance matrix of the residuals of the regression,
specified as an
CovarFormat — Format for covariance matrix
'full' (default) | character vector with value
(Optional) Format for the covariance matrix, specified as a character vector. The choices are:
'full'— This is the default method that computes the full covariance matrix.
'diagonal'— This forces the covariance matrix to be a diagonal matrix.
Objective — Least-squares objective function
Least-squares objective function, returned as scalar.
Introduced in R2006a