Results 11 to 20 of about 617,695 (198)
Estimating model error covariances using particle filters [PDF]
Quarterly Journal of the Royal Meteorological Society, 2017 A method is presented for estimating the error covariance of the errors in the model equations in observation space. Estimating model errors in this systematic way opens up the possibility to use data assimilation for systematic model improvement at the
Van Leeuwen, Peter J. +2 more
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Errors on errors – Estimating cosmological parameter covariance [PDF]
Proceedings of the International Astronomical Union, 2014 AbstractCurrent and forthcoming cosmological data analyses share the challenge of huge datasets alongside increasingly tight requirements on the precision and accuracy of extracted cosmological parameters. The community is becoming increasingly aware that these requirements not only apply to the central values of parameters but, equally important, also
Joachimi, Benjamin, Taylor, Andy
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Covariate Measurement Error in Logistic Regression. [PDF]
The Annals of Statistics, 1985 In a logistic regression model when covariates are subject to measurement error the naive estimator, obtained by regressing on the observed covariates, is asymptotically biased. We introduce a bias-adjusted estimator and two estimators appropriate for normally distributed measurement errors - a functional maximum likelihood estimator and an estimator ...
Stefanski, Leonard A. +1 more
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Cox Regression with Dependent Error in Covariates [PDF]
Biometrics, 2017 SummaryMany survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation.
Yijian Huang, Ching-Yun Wang
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Linear Model Selection When Covariates Contain Errors [PDF]
Journal of the American Statistical Association, 2017 Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters.
Xinyu, Zhang +3 more
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An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter [PDF]
Nonlinear Processes in Geophysics, 2017 The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts.
G. Wu, G. Wu, X. Zheng
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The Effect of Spatially Correlated Errors on Sea Surface Height Retrieval from SWOT Altimetry
Remote Sensing, 2023 The upcoming technology of wide-swath altimetry from space will enable monitoring the ocean surface at 4–5 times better spatial resolution and 2–3 times better accuracy than traditional nadir altimeters. This development will provide a chance to directly
Max Yaremchuk +4 more
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Analysis error covarianceversusposterior covariance in variational data assimilation [PDF]
Quarterly Journal of the Royal Meteorological Society, 2012 AbstractThe problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The data contain errors (observation and background errors); hence there is an error in the analysis.
Gejadze, Igor +2 more
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Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment
Earth and Space Science, 2021 We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors.
Subhomoy Ghosh +3 more
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Nihon Kikai Gakkai ronbunshu, 2022 In robotic thick plate welding, the welding strength is ensured by periodically oscillating the welding torch attached to the end-effector in the horizontal direction, which is called weaving motion.
Masafumi OKADA +2 more
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