In \cite{KumarS15J2}, it was shown that a generalized maximum likelihood estimation problem on a (canonical) $��$-power-law model ($\mathbb{M}^{(��)}$-family) can be solved by solving a system of linear equations. This was due to an orthogonality relationship between the $\mathbb{M}^{(��)}$-family and a linear family with respect to the relative $��$-entropy (or the $\mathscr{I}_��$-divergence). Relative $��$-entropy is a generalization of the usual relative entropy (or the Kullback-Leibler divergence). $\mathbb{M}^{(��)}$-family is a generalization of the usual exponential family. In this paper, we first generalize the $\mathbb{M}^{(��)}$-family including the multivariate, continuous case and show that the Student-t distributions fall in this family. We then extend the above stated result of \cite{KumarS15J2} to the general $\mathbb{M}^{(��)}$-family. Finally we apply this result to the Student-t distribution and find generalized estimators for its parameters.

6 pages, Submitted to ISIT 2018