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An Application of Error-Covariance Analysis to Inertial Platform Errors

1986 American Control Conference, 1986
An error-covariance analysis procedure for application to inertial platform measurement errors is developed. Differences from the standard white-noise error-propagation results are noted. Implementation procedures and the application of the method to typical "generic" trajectories are discussed.
Shirley J. Tucker, Henry E. Stern
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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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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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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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Implicit treatment of model error using inflated observation‐error covariance

Quarterly Journal of the Royal Meteorological Society, 2017
Data assimilation involving imperfect dynamical models is an important topic in meteorology, oceanography and other geophysical applications. In filtering methods, the model error is compensated for by inflation. In variational data assimilation, authors usually try to estimate it, which means that all uncertainty‐loaded model inputs are included into ...
Gejadze, I., Oubanas, H., Shutyaev, V.
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Impact Evaluation Using Analysis of Covariance With Error-Prone Covariates That Violate Surrogacy

Evaluation Review, 2019
Background: Analysis of covariance (ANCOVA) is commonly used to adjust for potential confounders in observational studies of intervention effects. Measurement error in the covariates used in ANCOVA models can lead to inconsistent estimators of intervention effects.
J. R. Lockwood, Daniel F. McCaffrey
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Modeling the random effects covariance matrix for longitudinal data with covariates measurement error

Statistics in Medicine, 2018
Longitudinal data occur frequently in practice such as medical studies and life sciences. Generalized linear mixed models (GLMMs) are commonly used to analyze such data. It is typically assumed that the random effects covariance matrix is constant among subjects in these models.
Md Erfanul Hoque, Mahmoud Torabi
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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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The Covariance Matrix of the Error Vector

2003
Assumption (iv) of the linear regression model claims the covariance matrix of the error vector ɛ to be Cov(ɛ) = σ2In with an unknown parameter σ2 ∈ (0, ∞). This chapter discusses the estimation of σ2 in detail, and introduces situations under which it appears to be reasonable to extend assumption (iv) to Cov(e) = σ2V for some symmetric positive ...
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The covariance matrix of ARMA errors in closed form

Journal of Econometrics, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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