Results 1 to 10 of about 108,015 (219)
Large Dimensional Analysis and Optimization of Robust Shrinkage Covariance Matrix Estimators [PDF]
, 2014 This article studies two regularized robust estimators of scatter matrices proposed (and proved to be well defined) in parallel in (Chen et al., 2011) and (Pascal et al., 2013), based on Tyler's robust M-estimator (Tyler, 1987) and on Ledoit and Wolf's ...
Couillet, Romain, McKay, Matthew R.
core +6 more sources
When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators [PDF]
, 2010 The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization.
Lillo, Fabrizio +3 more
core +4 more sources
New properties for Tyler's covariance matrix estimator [PDF]
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016 In this paper, we deal with covariance matrix estimation in complex elliptically symmetric (CES) distributions. We focus on Tyler's estimator (TyE) and the well-known sample covariance matrix (SCM). TyE is widely used in practice, but its statistical behavior is still poorly understood.
Gordana Drašković, Frédéric Pascal
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Econometric Computing with HC and HAC Covariance Matrix Estimators
Journal of Statistical Software, 2004 Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the ...
Achim Zeileis
doaj +4 more sources
First Passage Time Covariance Matrix Estimators
SSRN Electronic Journal, 2021 We devise a new high-frequency covariance matrix estimator based on price durations which is guaranteed to be positive-definite. Both non-parametric and parametric versions are proposed. A comprehensive Monte Carlo simulation shows that this class of estimators are less biased, more efficient, and generate lower RMSE as well as QLIKE errors ...
Seok Young Hong +2 more
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Fitting Penalized Estimator for Sparse Covariance Matrix with Left-Censored Data by the EM Algorithm [PDF]
MathematicsEstimating the sparse covariance matrix can effectively identify important features and patterns, and traditional estimation methods require complete data vectors on all subjects.
Shanyi Lin +4 more
doaj +2 more sources
Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Entropy, 2022 This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate ...
Bin Zhang, Hengzhen Huang, Jianbin Chen
doaj +1 more source
Asset Allocation Strategies Using Covariance Matrix Estimators
Acta Universitatis Sapientiae: Economics and Business, 2022 The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high ...
László PáL
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
openaire +3 more sources
Covariance estimation via fiducial inference
Statistical Theory and Related Fields, 2021 As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi +3 more
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