Results 11 to 20 of about 534,560 (282)
Adaptive Covariance Estimation with model selection [PDF]
Mathematical Methods of Statistics, 2012 We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al.
A. G. Journel +12 more
core +7 more sources
Deep Covariance Estimation Hashing [PDF]
IEEE Access, 2019 Deep hashing, the combination of advanced convolutional neural networks and efficient hashing, has recently achieved impressive performance for image retrieval.
Yue Wu +5 more
doaj +2 more sources
Geodesically Parameterized Covariance Estimation [PDF]
SIAM Journal on Matrix Analysis and Applications, 2021 Statistical modeling of spatiotemporal phenomena often requires selecting a covariance matrix from a covariance class. Yet standard parametric covariance families can be insufficiently flexible for practical applications, while non-parametric approaches may not easily allow certain kinds of prior knowledge to be incorporated.
Musolas, Antoni +2 more
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Condition Number Regularized Covariance Estimation. [PDF]
J R Stat Soc Series B Stat Methodol, 2013 SummaryEstimation of high dimensional covariance matrices is known to be a difficult problem, has many applications and is of current interest to the larger statistics community. In many applications including the so-called ‘large p, small n’ setting, the estimate of the covariance matrix is required to be not only invertible but also well conditioned.
Won JH, Lim J, Kim SJ, Rajaratnam B.
europepmc +4 more sources
Estimation of Bergsma’s covariance
Journal of the Korean Statistical Society, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Arup Bose +2 more
openaire +1 more source
List-decodable covariance estimation
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, 2022 Abstract slightly clipped.
Ivkov, Misha, Kothari, Pravesh K.
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Estimating cosmological parameter covariance [PDF]
Monthly Notices of the Royal Astronomical Society, 2014 We investigate the bias and error in estimates of the cosmological parameter covariance matrix, due to sampling or modelling the data covariance matrix, for likelihood width and peak scatter estimators. We show that these estimators do not coincide unless the data covariance is exactly known. For sampled data covariances, with Gaussian distributed data
Taylor, Andy, Joachimi, Benjamin
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Shrinkage Estimators for Covariance Matrices [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels, Michael J., Kass, Robert E.
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SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES [PDF]
Econometric Theory, 2011 We study a general class of semiparametric estimators when the infinite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametrically using generated covariates. Such estimators are used frequently to e.g., estimate nonlinear models with endogenous covariates when identification is achieved using ...
Mammen, Enno +2 more
openaire +14 more sources
Beta-Adjusted Covariance Estimation [PDF]
SSRN Electronic Journal, 2021 The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks ...
Boudt, Kris +3 more
openaire +2 more sources