The Annals of Statistics
- Ann. Statist.
- Volume 10, Number 1 (1982), 256-265.
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 is given, when these estimates are based on single observed vectors from each of the $n$ populations and an independent estimate of the common covariance matrix. A new derivation of least squares estimates is also given.
Ann. Statist., Volume 10, Number 1 (1982), 256-265.
First available in Project Euclid: 12 April 2007
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Villegas, C. Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models. Ann. Statist. 10 (1982), no. 1, 256--265. doi:10.1214/aos/1176345708. https://projecteuclid.org/euclid.aos/1176345708