Displaying similar documents to “Ridge estimation of covariance matrix from data in two classes”

Regularization for high-dimensional covariance matrix

Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, Defei Zhang, Jianxin Pan (2016)

Special Matrices

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In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n. For highdimensional data, the underlying structures of certain covariance matrix estimates are usually blurred due to substantial random noises, which is an obstacle to draw statistical inferences. In this paper, we propose a method to identify the underlying covariance structure by regularizing...

Variance components and an additional experiment

Lubomír Kubáček (2012)

Applications of Mathematics

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Estimators of parameters of an investigated object can be considered after some time as insufficiently precise. Therefore, an additional measurement must be realized. A model of a measurement, taking into account both the original results and the new ones, has a litle more complicated covariance matrix, since the variance components occur in it. How to deal with them is the aim of the paper.

Uncertainty of coordinates and looking for dispersion of GPS receiver

Pavel Tuček, Jaroslav Marek (2006)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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The aim of the paper is to show some possible statistical solution of the estimation of the dispersion of the GPS receiver. The presented method (based on theory of linear model with additional constraints of type I) can serve for an improvement of the accuracy of estimators of coordinates acquired from the GPS receiver.

Aspects of multivariate regression.

Philip J. Brown (1980)

Trabajos de Estadística e Investigación Operativa

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Important features of multivariate linear regression are emphasised and a selection of prior distributions discussed. Priors used by Brown and Zidek (1978) lead them to a class of 'empirical' Bayes shrinkage estimates. The strength of shrinkage is examined with respect to an election forecasting example where observations obtain one after another.