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mvnrfish

Fisher information matrix for multivariate normal or least-squares regression

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Syntax

Fisher = mvnrfish(Data,Design,Covariance)
Fisher = mvnrfish(___,MatrixFormat,CovarFormat)

Description

Fisher = mvnrfish(Data,Design,Covariance) computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates.

Fisher = mvnrfish(___,MatrixFormat,CovarFormat) computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates using optional arguments.

Fisher is a TOTALPARAMS-by-TOTALPARAMS Fisher information matrix. The size of TOTALPARAMS depends on MatrixFormat and on current parameter estimates. If MatrixFormat = 'full',

TOTALPARAMS = NUMPARAMS + NUMSERIES * (NUMSERIES + 1)/2

If MatrixFormat = 'paramonly',

TOTALPARAMS = NUMPARAMS

Note

mvnrfish operates slowly if you calculate the full Fisher information matrix.

Input Arguments

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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, the sample is ignored. (Use mvnrmle to handle missing data.)

Data Types: double

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

Estimates for the covariance of the residuals of the regression, specified as an NUMSERIES-by-NUMSERIES matrix.

Data Types: double

(Optional) Parameters to be included in the Fisher information matrix, specified as a character vector. The choices are:

  • 'full' — This is the default method that computes the full Fisher information matrix for both model and covariance parameter estimates.

  • 'paramonly' — This computes only components of the Fisher information matrix associated with the model parameter estimates.

Data Types: char

(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.

Data Types: char

Output Arguments

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Fisher information matrix, returned as an TOTALPARAMS-by-TOTALPARAMS matrix.

Version History

Introduced in R2006a

See Also

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