Results 271 to 280 of about 147,262 (309)

Cox Regression with Covariate Measurement Error

Scandinavian Journal of Statistics, 2002
This article deals with parameter estimation in the Cox proportional hazards model when covariates are measured with error. We consider both the classical additive measurement error model and a more general model which represents the mis‐measured version of the covariate as an arbitrary linear function of the true covariate plus random noise.
Hu, Chengcheng, Lin, D. Y.
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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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A Longitudinal Measurement Error Model with a Semicontinuous Covariate

Biometrics, 2005
Summary Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep‐disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed
Li, Liang, Shao, Jun, Palta, Mari
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Covariate‐based cepstral parameterizations for time‐varying spatial error covariances

Environmetrics, 2014
The difference between a mechanistic model of a spatio‐temporal process and associated observations can reasonably be represented by a random process with possible dependence structure in space and time. Often, such error processes are assumed to be stationary in time, so that a unique spatial error covariance structure is applicable for all time ...
Gladish, D. W., Wikle, C. K., Holan, S. H.   +2 more
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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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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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