Multivariate linear regression

returns
the estimated coefficients for a multivariate normal regression of
the `beta`

= mvregress(`X`

,`Y`

)*d*-dimensional responses in `Y`

on
the design matrices in `X`

.

returns
the estimated coefficients using additional options specified by one
or more name-value pair arguments. For example, you can specify the
estimation algorithm, initial estimate values, or maximum number of
iterations for the regression.`beta`

= mvregress(`X`

,`Y`

,`Name,Value`

)

[1] Little, Roderick J. A., and Donald B.
Rubin. *Statistical Analysis with Missing Data*.
2nd ed., Hoboken, NJ: John Wiley & Sons, Inc., 2002.

[2] Meng, Xiao-Li, and Donald B. Rubin. “Maximum
Likelihood Estimation via the ECM Algorithm.” *Biometrika*.
Vol. 80, No. 2, 1993, pp. 267–278.

[3] Sexton, Joe, and A. R. Swensen. “ECM
Algorithms that Converge at the Rate of EM.” *Biometrika*.
Vol. 87, No. 3, 2000, pp. 651–662.

[4] Dempster, A. P., N. M. Laird, and D. B.
Rubin. “Maximum Likelihood from Incomplete Data via the EM
Algorithm.” *Journal of the Royal Statistical Society*.
Series B, Vol. 39, No. 1, 1977, pp. 1–37.