Results 281 to 290 of about 147,262 (309)

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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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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Correction for Covariate Measurement Error in Nonparametric Longitudinal Regression

Biometrics, 2010
Summary We introduce a correction for covariate measurement error in nonparametric regression applied to longitudinal binary data arising from a study on human sleep. The data have been surveyed to investigate the association of some hormonal levels and the probability of being asleep.
Rummel, D., Augustin, T., Küchenhoff, H.   +2 more
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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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Repeated Probit Regression When Covariates Are Measured With Error

Biometrics, 1999
Summary. This paper develops a model for repeated binary regression when a covariate is measured with error. The model allows for estimating the effect of the true value of the covariate on a repeated binary response. The choice of a probit link for the effect of the error‐free covariate, coupled with normal measurement error for the error‐free ...
Follmann, Dean A., Hunsberger, Sally A., Albert, Paul S.   +2 more
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Error covariance bounds for suboptimal filters with Lipschitzian drift and Poisson-sampled measurements

Automatica, 2020
Aneel Tanwani, Olga Yufereva
exaly  

A New Look at Boundedness of Error Covariance of Kalman Filtering

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018
Wangyan Li, Guoliang Wei, Derui Ding
exaly  

Maximum likelihood estimation of inflation factors on error covariance matrices for ensemble Kalman filter assimilation

Quarterly Journal of the Royal Meteorological Society, 2012
Xiaogu Zheng, Shupeng Zhang, Guocan Wu   +2 more
exaly