Robust Covariance Matrix Estimation Via Robust Estimation Of Principal Components
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Robust Covariance Matrix Estimation Via Robust Estimation Of Principal Components
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Author : Jacqueline S. Galpin
language : en
Publisher:
Release Date : 1986
Robust Covariance Matrix Estimation Via Robust Estimation Of Principal Components written by Jacqueline S. Galpin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.
Robust Principal Components And Dispersion Matrices Via Projection Pursuit
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Author : Zhonglian Chen
language : en
Publisher:
Release Date : 1981
Robust Principal Components And Dispersion Matrices Via Projection Pursuit written by Zhonglian Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with categories.
This paper discusses a new kind of robust procedure for estimating covariance/correlation matrices and their principal components. Robust eigenvectors and eigenvalues of a covariance matrix are obtained by the projection pursuit method (PP) with robust variance as a projection index. Monte Carlo simulation results show that the best of the three projection pursuit type procedures introduced in this study compares favorably with approaches based on M-estimators of covariance: the estimate obtained by the new procedure has about the same bias and variance as the best M-estimators, and a somewhat better breakdown point. (Author).
Robust Estimation Of A High Dimensional Integrated Covariance Matrix
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Author : Takayuki Morimoto
language : en
Publisher:
Release Date : 2015
Robust Estimation Of A High Dimensional Integrated Covariance Matrix written by Takayuki Morimoto and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
In this paper, we consider a robust method of estimating a realized covariance matrix calculated as the sum of cross products of intraday high-frequency returns. According to recent papers in financial econometrics, the realized covariance matrix is essentially contaminated with market microstructure noise. Although techniques for removing noise from the matrix have been studied since the early 2000s, they have primarily investigated a low-dimensional covariance matrix with statistically significant sample sizes. We focus on noise-robust covariance estimation under converse circumstances; that is, a high-dimensional covariance matrix possibly with a small sample size. For the estimation, we utilize a statistical hypothesis test based on the characteristic that the largest eigenvalue of the covariance matrix asymptotically follows a Tracy-Widom distribution. The null hypothesis assumes that log returns are not pure noises. If a sample eigenvalue is larger than the relevant critical value, then we fail to reject the null hypothesis. The simulation results show that the estimator studied here performs better than others as measured by mean squared error. The empirical analysis shows that our proposed estimator can be adopted to forecast future covariance matrices using real data.
A Practitioner S Guide To Robust Covariance Matrix Estimation
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Author : Wouter J. Den Haan
language : en
Publisher:
Release Date : 1996
A Practitioner S Guide To Robust Covariance Matrix Estimation written by Wouter J. Den Haan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Analysis of covariance categories.
This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimators and test statistics when some of the parameters are well identified, but others are poorly identified because of weak instruments. The asymptotic theory entails applying empirical process theory to obtain a limiting representation of the (concentrated) objective function as a stochastic process. The general results are specialized to two leading cases, linear instrumental variables regression and GMM estimation of Euler equations obtained from the consumption-based capital asset pricing model with power utility. Numerical results of the latter model confirm that finite sample distributions can deviate substantially from normality, and indicate that these deviations are captured by the weak instruments asymptotic approximations.
Structured Robust Covariance Estimation
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Author : Ami Wiesel
language : en
Publisher:
Release Date : 2015-12-04
Structured Robust Covariance Estimation written by Ami Wiesel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-04 with Technology & Engineering categories.
We consider robust covariance estimation with an emphasis on Tyler's M-estimator. This method provides accurate inference of an unknown covariance in non-standard settings, including heavy-tailed distributions and outlier contaminated scenarios. We begin with a survey of the estimator and its various derivations in the classical unconstrained settings. The latter rely on the theory of g-convex analysis which we briefly review. Building on this background, we enhance robust covariance estimation via g-convex regularization, and allow accurate inference using a smaller number of samples. We consider shrinkage, diagonal loading, and prior knowledge in the form of symmetry and Kronecker structures. We introduce these concepts to the world of robust covariance estimation, and demonstrate how to exploit them in a computationally and statistically efficient manner.
Robust Covariance Matrix Estimation With Data Dependent Var Prewhitening Order
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Author : Wouter J. Den Haan
language : en
Publisher:
Release Date : 2000
Robust Covariance Matrix Estimation With Data Dependent Var Prewhitening Order written by Wouter J. Den Haan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Analysis of covariance categories.
This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and we demonstrate the benefits of using a model selection criterion (either AIC or BIC) to determine its lag structure. Furthermore, once data-dependent VAR prewhitening has been utilized, we find negligible or even counter-productive effects of applying standard kernel-based methods to the prewhitened residuals; that is, the performance of the prewhitened kernel estimator is virtually indistinguishable from that of the VARHAC estimator.
Robust Estimation For The Covariance Matrix Of Multi Variate Time Series
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Author : Byung-soo Kim
language : en
Publisher:
Release Date : 2011
Robust Estimation For The Covariance Matrix Of Multi Variate Time Series written by Byung-soo Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
In this article, we study the robust estimation for the covariance matrix of stationary multi-variate time series. As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (1998). Particularly, the MDPDE is designed to perform properly when the time series is Gaussian. As a special case, we consider the robust estimator for the autocovariance function of univariate stationary time series. It is shown that the MDPDE is strongly consistent and asymptotically normal under regularity conditions. Simulation results are provided for illustration.
A Practitioner S Guide To Robust Covariance Matrix Estimation
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Author : Wouter J. den Haan
language : en
Publisher:
Release Date : 1900
A Practitioner S Guide To Robust Covariance Matrix Estimation written by Wouter J. den Haan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1900 with categories.
High Dimensional Covariance Estimation
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Author : Mohsen Pourahmadi
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-24
High Dimensional Covariance Estimation written by Mohsen Pourahmadi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-24 with Mathematics categories.
Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.
Robust Estimation Of Constrained Covariance Matrices For Confirmatory Factor Analysis
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Author : Elise Dupuis Lozeron
language : en
Publisher:
Release Date : 2009
Robust Estimation Of Constrained Covariance Matrices For Confirmatory Factor Analysis written by Elise Dupuis Lozeron and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.