Results 231 to 240 of about 9,759 (265)

ESTIMATING A COVARIANCE MATRIX IN MANOVA MODEL

Statistics & Risk Modeling, 2002
Summary: For the estimation of the covariance matrix in the framework of multivariate analysis of variance (MANOVA) model, \textit{B.K. Sinha} and \textit{M. Ghosh} [ibid. 5, 201-227 (1987; Zbl 0634.62050)] proposed a Stein type truncated estimator improving on the uniformly minimum variance unbiased (UMVU) estimator under the entropy loss.
openaire   +2 more sources

Covariance Matrix Estimation

2016
Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
openaire   +1 more source

A new estimator of covariance matrix

Journal of Statistical Planning and Inference, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ma, Tiefeng, Jia, Lijie, Su, Yingsheng
openaire   +1 more source

Enhanced Covariance Matrix Estimators in Adaptive Beamforming

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is ...
Richard Abrahamsson, Yngve Selén, Petre Stoica   +2 more
openaire   +1 more source

A Robust Heteroskedasticity Consistent Covariance Matrix Estimator

Statistics, 1997
To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification ...
openaire   +3 more sources

On the estimation of a covariance matrix in designing Parzen classifiers

Pattern Recognition, 1996
The design of the Parzen classifiers requires careful attention to the window-width as well as kernel covariance matrices. Although a considerable amount of effort has been devoted to the selection of the window-width, the problem of estimating kernel covariance matrices has received little attention in the past.
Yoshihiko Hamamoto, Yasushi Fujimoto, Shingo Tomita   +2 more
openaire   +1 more source

Sparse and Low-Rank Covariance Matrix Estimation

Journal of the Operations Research Society of China, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhou, Shenglong, Kong, LingChen, Luo, Ziyan, Xiu, Naihua   +3 more
openaire   +3 more sources

Covariance matrix estimation for left-censored data

Computational Statistics & Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maiju Pesonen, Henri Pesonen, Jaakko Nevalainen   +2 more
openaire   +1 more source

Equivariant estimators of the covariance matrix

Canadian Journal of Statistics, 1990
AbstractGiven a Wishart matrix S [S ∽ Wp(n, Σ)] and an independent multinomial vector X [X ∽ Np (μ, Σ)], equivariant estimators of Σ are proposed. These estimators dominate the best multiple of S and the Stein‐type truncated estimators.
openaire   +1 more source

An Estimator of Normal Covariance Matrix

Calcutta Statistical Association Bulletin, 1980
Solliah (1964), by considering the group of lower triangular matrices, suggested an estimator of the normal covariance matrix Σ when the mean vector is known and the loss function is tr [Formula: see text] His estimator besides being minimax is better than the MLB of Σ.
openaire   +1 more source