Results 161 to 170 of about 617,695 (198)

Generalised Covariance Analysis with Unequal Error Variances

Biometrics, 1969
This paper is concerned with the application of the general linear model to the situation in which the observations are divided into several groups. It is assumed that some of the regression coefficients may be common to all groups whilst other regression coefficients and also the error variance may vary from group to group.
J R, Ashford, S, Brown
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Adjusting for covariate errors with nonparametric assessment of the true covariate distribution

Biometrika, 2004
Summary: A well-known and useful method for generalised regression analysis when a linear covariate \(x\) is available only through some approximation \(z\) is to carry out more or less the usual analysis with \(E(x\,|\,z)\) substituted for \(x\).
Pierce, Donald A., Kellerer, Albrecht M.
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Background error covariances

2014
Abstract This chapter deals with the estimation and specification of realistic background error covariances, which is a key issue in data assimilation, since these covariances are used to filter and propagate observations. The underlying equations of error evolution are summarized, and associated simulation techniques are also presented,
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Covariate measurement error in generalized linear models

Biometrika, 1987
The EM algorithm is used to obtain estimators of regression coefficients for generalized linear models with canonical link when normally distributed covariates are masked by normally distributed measurement errors. By casting the true covariates as 'missing data', the EM procedure suggests an iterative scheme in which each cycle consists of an E-step ...
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Altimeter Covariances and Errors Treatment

2003
There are now a large number of data sources which are being or soon will be used in ocean data assimilation systems for determining the ocean circulation. The following is not a complete list but indicates some of the most important data sources.
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Robust estimation of measurement error covariance

Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393), 2002
Conventional indirect estimations of measurement error covariance are very sensitive to gross errors. A robust indirect algorithm based on Hampel's three-part redescending M-estimators is developed. Credible results can be achieved either with or without the presence of external causes.
null Zhao Yuhong, null Gu Zhongwen
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Data error covariance in matched-field geoacoustic inversion

The Journal of the Acoustical Society of America, 2006
Many approaches to geoacoustic inversion are based implicitly on the assumptions that data errors are Gaussian-distributed and spatially uncorrelated (i.e., have a diagonal covariance matrix). However, the latter assumption is often not valid due to theory errors, and can lead to reduced accuracy for geoacoustic parameter estimates and underestimation ...
Stan E, Dosso, Peter L, Nielsen, Michael J, Wilmut   +2 more
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ERROR ANALYSIS BY THE COVARIANCE METHOD

1963
Abstract : The analysis of dependent errors makes use of the concept of distribution moments and the moment matrix (covariance matrix). This paper presents an analysis of the normal bivariate and trivariate error distributions along with their relationships to the moment matrix, and the application of this concept to least squares and adjustments.
Donald A. Richardson, Melvin E. Shultz
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Minimum mean-squared error covariance shaping

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
The paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense.
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Covariance analysis in generalized linear measurement error models

Statistics in Medicine, 1989
AbstractWe summarize some of the recent work on the errors‐in‐variables problem in generalized linear models. The focus is on covariance analysis, and in particular testing for and estimation of treatment effects. There is a considerable difference between the randomized and non‐randomized models when testing for an effect.
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