Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models
Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models
In a linear (or affine) functional model the principal parameter is a subspace (respectively an affine subspace) in a finite dimensional inner product space, which contains the means of $n$ multivariate normal populations, all having the same covariance matrix. A relatively simple, essentially algebraic derivation of the maximum likelihood estimates …