Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models
Villegas, C.
Ann. Statist., Tome 10 (1982) no. 1, p. 256-265 / Harvested from Project Euclid
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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.
Publié le : 1982-03-14
Classification:  Bayesian inference,  logical priors,  inner statistical inference,  invariance,  conditional confidence,  multivariate analysis,  62A05,  62F15 
@article{1176345708,
     author = {Villegas, C.},
     title = {Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models},
     journal = {Ann. Statist.},
     volume = {10},
     number = {1},
     year = {1982},
     pages = { 256-265},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176345708}
}

Villegas, C. Maximum Likelihood and Least Squares Estimation in Linear and Affine Functional Models. Ann. Statist., Tome 10 (1982) no. 1, pp.  256-265. http://gdmltest.u-ga.fr/item/1176345708/