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March, 1982 Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models
C. Villegas
Ann. Statist. 10(1): 256-265 (March, 1982). DOI: 10.1214/aos/1176345708

Abstract

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.

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C. Villegas. "Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models." Ann. Statist. 10 (1) 256 - 265, March, 1982. https://doi.org/10.1214/aos/1176345708

Information

Published: March, 1982
First available in Project Euclid: 12 April 2007

zbMATH: 0501.62020
MathSciNet: MR642737
Digital Object Identifier: 10.1214/aos/1176345708

Subjects:
Primary: 62A05
Secondary: 62F15

Rights: Copyright © 1982 Institute of Mathematical Statistics

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Vol.10 • No. 1 • March, 1982
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