| Financial Toolbox™ | ![]() |
Fisher information matrix for multivariate normal or least-squares regression
Fisher = mvnrfish(Data, Design, Covariance, MatrixFormat, CovarFormat)
Data | 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. |
Design | A matrix or a cell array that handles two model structures:
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Covariance | NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the residuals of the regression. |
MatrixFormat | (Optional) String that identifies parameters to be included in the Fisher information matrix:
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CovarFormat | (Optional) String that specifies the format for the covariance matrix. The choices are:
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Fisher = mvnrfish(Data, Design, Covariance, MatrixFormat, CovarFormat) computes a Fisher information matrix based on current maximum likelihood or least-squares parameter estimates.
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. |
See Multivariate Normal Linear Regression.
![]() | mtimes | mvnrmle | ![]() |
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