Results 81 to 90 of about 547,601 (228)
IEEE Access, 2019 Direction-of-arrival (DOA) estimation using nested linear array ignores the information from repeated sensors, thus involves the problem of losing accuracy. Moreover, non-circular feature of signals is rarely considered and it results in discontinuity of
Peng Han +3 more
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An Introduction to Shrinkage Estimation of the Covariance Matrix: A Pedagogic Illustration
Spreadsheets in Education, 2011 Shrinkage estimation of the covariance matrix of asset returns was introduced to the finance profession several years ago. Since then, the approach has also received considerable attention in various life science studies, as a remedial measure for ...
Clarence C. Y. Kwan
doaj
Large Covariance Matrix Estimation by Composite Minimization [PDF]
, 2015 The present thesis concerns large covariance matrix estimation via composite minimization under the assumption of low rank plus sparse structure. Existing methods like POET (Principal Orthogonal complEment Thresholding) perform estimation by extracting principal components and then applying a soft thresholding algorithm.
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Remote SensingGridless direction of arrival (DOA) estimation methods have garnered significant attention due to their ability to avoid grid mismatch errors, which can adversely affect the performance of high-resolution DOA estimation algorithms. However, most existing
Yuan Cao, Tianjun Zhou, Qunfei Zhang
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Quantifying lost information due to covariance matrix estimation in parameter inference [PDF]
, 2016 Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which ...
E. Sellentin, A. Heavens
semanticscholar +1 more source
Size Matters: Covariance Matrix Estimation Under the Alternative [PDF]
SSRN Electronic Journal, 2005 Summary: The purpose of this paper is to investigate, using Monte Carlo methods, whether \textit{A. R. Hall}'s [Econometrica 68, No. 6, 1517--1527 (2000; Zbl 1015.62123)] centred test of overidentifying restrictions for parameters estimated by the generalized method of moments (GMM) is more powerful, once the test is size-adjusted, than the standard ...
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Channel Covariance Identification in FDD Massive MIMO Systems
Proceedings, 2018 Channel estimation for Massive MIMO systems has drawn a lot of attention in the last years. A number of estimation methods rely on the knowledge of the channel covariance matrix to operate. However, this covariance is not known in practice, and it should
José P. González-Coma +3 more
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Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation [PDF]
Selecting an estimator for the variance covariance matrix is an important step in hypothesis testing. From less robust to more robust, the available choices include: Eicker/White heteroskedasticity-robust standard errors, Newey and West ...
Mikko Packalen, Tony Wirjanto
core
Robust Covariance Adaptation in Adaptive Importance Sampling
, 2018 Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution.
Bugallo, Monica F. +2 more
core +1 more source
International Journal of Adaptive Control and Signal Processing, 2019 This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems.
Xiao Zhang, F. Ding, Erfu Yang
semanticscholar +1 more source