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In this paper, some distribution in the family of those with invariance under orthogonal transformations within ans-dimensional linear subspace are characterized by maximun likelihood criteria. Specially, the main result is: supposeP v is a projection matrix of a givens-dimensional subspaceV, andx 1, ...,x n > are i.i.d. samples drawn from population with a pdff(x′P v x), wheref(·) is a positive and continuously differentiable function. ThenP v (M n ) is the maximum likelihood estimator ofP v iff $$f(x) = c_k exp(kx)(k > 0)$$ where $$M_n = \sum\limits_{i = 1}^n {x_i x'_i ,P_u (M_n ) = \sum\limits_{i = 1}^n {\hat \xi _i \hat \xi '_i ,\lambda _1 , \cdot \cdot \cdot ,\lambda _2 } } $$ are the firsts largest eigenvalues of matrixM n , and $$\hat \xi _1 , \cdot \cdot \cdot ,\hat \xi _2 $$ , are their associated eigenvectors.
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Jul 13, 2005
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