Abstract
Let X1,...,Xn(n > p) be a random sample from multivariate normal distribution Np(μ, Σ), where μ ∈ Rp and Σ is a positive definite matrix, both μ and Σ being unknown. We consider the problem of estimating the precision matrix Σ-1. In this paper it, is shown that for the entropy loss, the best lower-triangular affine equivariant minimax estimator of Σ-1 is inadmissible and an improved estimator is explicitly constructed. Note that our improved estimator is obtained from the class of lower-triangular scale equivariant estimators.
Original language | English |
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Pages (from-to) | 760-768 |
Number of pages | 9 |
Journal | Annals of the Institute of Statistical Mathematics |
Volume | 53 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2001 |
Externally published | Yes |
Keywords
- Best lower-triangular equivariant minimax estimator
- Inadmissibility
- Multivariate normal distribution
- Precision matrix
- Risk function
- The entropy loss