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Multivariate Normal Regression

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

Functions

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
ecmlsrmleLeast-squares 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

Examples and How To

Multivariate Normal Regression Functions

Financial Toolbox™ has a number of functions for multivariate normal regression with or without missing data.

Portfolios with Missing Data

This example illustrates how to use the missing data algorithms for portfolio optimization and for valuation.

Valuation with Missing Data

Estimating the coefficients of the Capital Asset Pricing Model with incomplete stock price data.

Capital Asset Pricing Model with Missing Data

This example illustrates implementation of the Capital Asset Pricing Model (CAPM) in the presence of missing data.

Concepts

Multivariate Normal Regression Types

Estimating the parameters of the regression model using multivariate normal regression.

Multivariate Normal Regression

Using likelihood-based methods for the multivariate normal regression model.

Maximum Likelihood Estimation with Missing Data

Estimating the parameters of the multivariate normal regression model using maximum likelihood estimation.

Troubleshooting

Troubleshooting Multivariate Normal Regression

Handling various technical and operational difficulties with multivariate normal regression.

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