A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit,Michael Wolf +1 more
TLDR
This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically, that is distribution-free and has a simple explicit formula that is easy to compute and interpret.About:
This article is published in Journal of Multivariate Analysis.The article was published on 2004-02-01 and is currently open access. It has received 2497 citations till now. The article focuses on the topics: Estimation of covariance matrices & Covariance function.read more
Citations
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Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
Journal ArticleDOI
Deep learning with convolutional neural networks for EEG decoding and visualization.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
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A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
TL;DR: This work proposes a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity and applies it to the problem of inferring large-scale gene association networks.
Journal ArticleDOI
Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection
TL;DR: In this paper, the covariance matrix of stock returns is estimated by an optimally weighted average of two existing estimators: the sample covariance and single-index covariance matrices.
Journal Article
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
TL;DR: It is proved that the problem of finding the configuration that maximizes mutual information is NP-complete, and a polynomial-time approximation is described that is within (1-1/e) of the optimum by exploiting the submodularity of mutual information.
References
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Large sample properties of generalized method of moments estimators
Book ChapterDOI
Estimation with Quadratic Loss
W. James,Charles Stein +1 more
TL;DR: In this paper, the authors consider the problem of finding the best unbiased estimator of a linear function of the mean of a set of observed random variables. And they show that for large samples the maximum likelihood estimator approximately minimizes the mean squared error when compared with other reasonable estimators.
Journal ArticleDOI
Distribution of eigenvalues for some sets of random matrices
V A Marčenko,Leonid Pastur +1 more
TL;DR: In this article, the authors studied the distribution of eigenvalues for two sets of random Hermitian matrices and one set of random unitary matrices in the energy spectra of disordered systems.
Journal ArticleDOI
Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection
TL;DR: In this paper, the covariance matrix of stock returns is estimated by an optimally weighted average of two existing estimators: the sample covariance and single-index covariance matrices.
Journal ArticleDOI
The Markowitz Optimization Enigma: Is ‘Optimized’ Optimal?
TL;DR: The Improving Portfolio Performance With Quantitative Models (IPPMQM) conference as mentioned in this paper was the first conference devoted to quantitative models for portfolio performance improvement, which was held in 1989.