Log-likelihood function for least-squares regression with missing data
Data — Data sample
Data sample, specified as an
random vector. If a data sample has missing
values, represented as
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 — User-supplied estimate for covariance matrix of residuals of the regression
(Optional) User-supplied estimate for the
covariance matrix of the residuals of the
regression, specified as an
ecmlsrobj requires that
ecmlsrobj(Data, Design, Parameters) = ecmmvnrobj(Data, Design, Parameters, IdentityMatrix)
IdentityMatrix is a
Objective — Least-squares objective function
Least-squares objective function, returned as scalar.
Introduced in R2006a