Evaluation and error analysis: Kalman gain regularization versus covariance regularization
نویسندگان
چکیده
منابع مشابه
Evaluation and error analysis: Kalman gain regularization versus covariance regularization
Ensemble size is critical to the efficiency and performance of the ensemble Kalman filter, but when the ensemble size is small, the Kalman gain generally cannot be well estimated. To reduce the negative effect of spurious correlations, a regularization process applied on either the covariance or the Kalman gain seems to be necessary. In this paper, we evaluate and compare the estimation errors ...
متن کاملCovariance Regularization by Thresholding
This paper considers regularizing a covariance matrix of p variables estimated from n observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a suitable sense, the variables are Gaussian or sub-Gaussian, and (log p)/n→ 0, and obtain explicit rates. The results are uniform over families of cov...
متن کاملQuantitative covariance NMR by regularization.
The square root of a covariance spectrum, which offers high spectral resolution along both dimensions requiring only few t (1) increments, yields in good approximation the idealized 2D FT spectrum provided that the amount of magnetization exchanged between spins is relatively small. When this condition is violated, 2D FT and covariance peak volumes may differ. A regularization method is present...
متن کاملDiscretization Error Analysis for Tikhonov Regularization
Received (Day Month Year) Revised (Day Month Year) We study the discretization of inverse problems defined by a Carleman operator. In particular we develop a discretization strategy for this class of inverse problems and we give a convergence analysis. Learning from examples as well as the discretization of integral equations can be analysed in our setting.
متن کاملCovariance Estimation: The GLM and Regularization Perspectives
Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data environment where enforcing the positive-definiteness constraint could be computationally expensive. We provide a survey of the progress made in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Geosciences
سال: 2011
ISSN: 1420-0597,1573-1499
DOI: 10.1007/s10596-010-9218-y