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Multivariate Normal Regression

Regression analysis, with or without missing data, using likelihood-based methods for multivariate normal regression

Use functions for regression analysis, with or without missing data, using likelihood-based methods for multivariate normal regression.

Functions

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ecmnfishFisher information matrix
ecmmvnrfishFisher information matrix for multivariate normal regression model
ecmnhessHessian of negative log-likelihood function
ecmninitInitial mean and covariance
ecmnobjMultivariate normal negative log-likelihood function
ecmnmleMean and covariance of incomplete multivariate normal data
ecmnstdStandard errors for mean and covariance of incomplete data
ecmmvnrstdEvaluate standard errors for multivariate normal regression model
mvnrmleMultivariate normal regression (ignore missing data)
ecmmvnrobjLog-likelihood function for multivariate normal regression with missing data
ecmlsrmleLeast-squares regression with missing data
ecmlsrobjLog-likelihood function for least-squares regression with missing data
ecmmvnrmleMultivariate normal regression with missing data
mvnrfishFisher information matrix for multivariate normal or least-squares regression
mvnrobjLog-likelihood function for multivariate normal regression without missing data
mvnrstdEvaluate standard errors for multivariate normal regression model
convert2surConvert multivariate normal regression model to seemingly unrelated regression (SUR) model

Topics

Troubleshooting

Troubleshooting Multivariate Normal Regression

Handling various technical and operational difficulties with multivariate normal regression.