Displaying similar documents to “Positive semidefiniteness of estimated covariance matrices in linear models for sample survey data”

Small-area estimation using adjustment by covariantes.

Nicholas T. Longford (1996)

Qüestiió

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Linear regression models with random effects are applied to estimating the population means of indirectly measured variables in small areas. The proposed method, a hybrid with design- and model-based elements, takes account of the area-level variation and of the uncertainty about the fitted regression model and the area-level population means of the covariates. The method is illustrated on data from the U.S. Department of Labor Literacy Surveys and is informally validated on two states,...

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.

Ridge estimation of covariance matrix from data in two classes

Yi Zhou, Bin Zhang (2024)

Applications of Mathematics

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This paper deals with the problem of estimating a covariance matrix from the data in two classes: (1) good data with the covariance matrix of interest and (2) contamination coming from a Gaussian distribution with a different covariance matrix. The ridge penalty is introduced to address the problem of high-dimensional challenges in estimating the covariance matrix from the two-class data model. A ridge estimator of the covariance matrix has a uniform expression and keeps positive-definite,...

Linear comparative calibration with correlated measurements

Gejza Wimmer, Viktor Witkovský (2007)

Kybernetika

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The paper deals with the linear comparative calibration problem, i. e. the situation when both variables are subject to errors. Considered is a quite general model which allows to include possibly correlated data (measurements). From statistical point of view the model could be represented by the linear errors-in-variables (EIV) model. We suggest an iterative algorithm for estimation the parameters of the analysis function (inverse of the calibration line) and we solve the problem of...

Bayesian joint modelling of the mean and covariance structures for normal longitudinal data.

Edilberto Cepeda-Cuervo, Vicente Nunez-Anton (2007)

SORT

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We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We propose a new and computationally efficient classic estimation method based on the Fisher scoring algorithm to obtain the maximum likelihood estimates of the parameters. In addition, we also propose a new and innovative Bayesian methodology based on the Gibbs sampling, properly adapted for...