Results 251 to 260 of about 402,138 (284)

Linguistically Described Covariance Matrix Estimation

2017
In this paper we present a covariance matrix estimation method based on linguistically described data samples. The linguistic variable describes a real data samples that could be used for a calculation of the covariance matrix. In most cases, real dataset contains noise samples that manifest as outliers.
Tomasz Przybyla, Tomasz Pander
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On the Maximum Likelihood Estimation of a Covariance Matrix

Mathematical Methods of Statistics, 2018
Stein phenomena (or Stein paradox) is as followed: ``there is a better estimator than the sample mean vector in the case of the multinormal mean vector under a quadratic loss function, and there is a better estimator than the sample covariance matrix in the case of multinormal covariance matrix under the Stein loss function.'' In this paper the set of ...
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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.
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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
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Covariance Matrix Estimation

2016
Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
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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
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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 ...
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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
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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
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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.
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