A maximum likelihood prediction function for the linear model with consistency results

An alternative technique to current methods for constructing a prediction function for the normal linear regression model is proposed based on the concept of maximum likelihood. The form of this prediction function is evaluated and normalized to produce a multivariate Student's t-density. Consistency properties are established under regularity conditions, and an empirical comparison, based on the Kullback-Leibler information divergence, is made with some other prediction functions.