Overview

The multivariate linear regression model expresses a d-dimensional continuous response vector as a linear combination of predictor terms plus a vector of error terms with a multivariate normal distribution. To fit multivariate linear regression models in Statistics and Machine Learning Toolbox, use mvregress. This function fits multivariate regression models with a diagonal (heteroscedastic) or unstructured (heteroscedastic and correlated) error variance-covariance matrix, S, using least squares or maximum likelihood estimation.Many variations of multivariate regression might not initially appear to be of the form supported by mvregress, such as: - Multivariate general linear model- Multivariate analysis of variance (MANOVA)- Longitudinal analysis- Panel data analysis- Seemingly unrelated regression (SUR)- Vector autoregressive (VAR) modelIn many cases, you can frame these problems in the form used by mvregress (but mvregress does not support parameterized error variance-covariance matrices). For the special case of one-way MANOVA, you can alternatively use manova1. Econometrics Toolbox has functions for VAR estimation.

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9781797063126

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