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Asymptotic theory of parameter estimation for Gauss-Markov random fields

Martin Janžura (1988)

Kybernetika

Asymptotic unbiased density estimators

Nicolas W. Hengartner, Éric Matzner-Løber (2009)

ESAIM: Probability and Statistics

This paper introduces a computationally tractable density estimator that has the same asymptotic variance as the classical Nadaraya-Watson density estimator but whose asymptotic bias is zero. We achieve this result using a two stage estimator that applies a multiplicative bias correction to an oversmooth pilot estimator. Simulations show that our asymptotic results are available for samples as low as n = 50, where we see an improvement of as much as 20% over the traditionnal estimator.

Asymptotical confidence region in a replicated mixed linear model with an estimated covariance matrix

Lubomír Kubáček (1988)

Mathematica Slovaca

Asymptotically Efficient Recursive Estimation for Incomplete Data Models Using the Observed Information.

T. Rydén (1998)

Metrika

Asymptotically minimax estimation of a constrained Poisson vector via polydisc transforms

Iain M. Johnstone, K. Brenda Macgibbon (1993)

Annales de l'I.H.P. Probabilités et statistiques

Asymptotically minimax estimation of order-constrained parameters and eigenfunctions of the laplacian on the ball

Adam Korányi, K. Brenda MacGibbon (2002)

Annales de l'I.H.P. Probabilités et statistiques

Asymptotically Most Powerful Unbiased Tests in the Independent not Identically Distributed Case

Andreas N. Philippou (1974)

Δελτίο της Ελληνικής Μαθηματικής Εταιρίας

Asymptotically normal confidence intervals for a determinant in a generalized multivariate Gauss-Markoff model

Wiktor Oktaba (1995)

Applications of Mathematics

By using three theorems (Oktaba and Kieloch [3]) and Theorem 2.2 (Srivastava and Khatri [4]) three results are given in formulas (2.1), (2.8) and (2.11). They present asymptotically normal confidence intervals for the determinant $| σ^{2} \sum |$ in the MGM model $(U, X B, σ^{2} \sum \otimes V)$ , $\sum > 0$ , scalar $σ^{2} > 0$ , with a matrix $V \geq 0$ . A known $n \times p$ random matrix $U$ has the expected value $E (U) = X B$ , where the $n \times d$ matrix $X$ is a known matrix of an experimental design, $B$ is an unknown $d \times p$ matrix of parameters and $σ^{2} \sum \otimes V$ is the covariance matrix of $U, \otimes$ being the symbol of the Kronecker...