Results 231 to 240 of about 531,665 (271)

A Nonparametric Prewhitened Covariance Estimator

Journal of Time Series Analysis, 2002
This paper proposes a new nonparametric spectral density estimator for time series models with general autocorrelation. The conventional nonparametric estimator that uses a positive kernel has mean squared error no better than n−4/5. We show that the best implementation of our estimator has mean squared error of order n−8/9, provided there is ...
Xiao, Zhijie, Linton, Oliver
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Estimating Mean Cost Using Auxiliary Covariates

Biometrics, 2011
We study the estimation of mean medical cost when censoring is dependent and a large amount of auxiliary information is present. Under missing at random assumption, we propose semiparametric working models to obtain low-dimensional summarized scores.
Pan, Wenqin, Zeng, Donglin
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Structured Robust Covariance Estimation

Foundations and Trends® in Signal Processing, 2015
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.
Wiesel, Ami, Zhang, Teng
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Robust Estimation of Multivariate Covariance Components

Biometrics, 2005
Summary In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood.
Dueck, Amylou, Lohr, Sharon
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Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance

IEEE Transactions on Industrial Electronics, 2014
The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises.
Bo Feng, Mengyin Fu, Hongbin Ma, Yuanqing Xia, Bo Wang   +4 more
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Estimating complex covariance matrices

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., 2005
The problem of estimating complex covariance matrices is considered. The objective is to obtain a well behaving estimator that circumvents the weaknesses of the standard sample covariance and regularized estimators. To this end, we use a variational technique that previously has been successfully applied in the real data case.
L. Svensson, M. Lundberg
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Estimation of proportional covariances

Statistics & Probability Letters, 1987
In the model for proportional covariance matrices of p-dimensional normally distributed random variables, the existence and uniqueness of the maximum likelihood estimator is established using convexity results.
Johansen, Søren, Tolver Jensen, Søren
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Sparse Steinian Covariance Estimation

Journal of Computational and Graphical Statistics, 2017
ABSTRACTWe consider a new method for sparse covariance matrix estimation which is motivated by previous results for the so-called Stein-type estimators. Stein proposed a method for regularizing the sample covariance matrix by shrinking together the eigenvalues; the amount of shrinkage is chosen to minimize an unbiased estimate of the risk (UBEOR) under
Brett Naul, Jonathan Taylor
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Estimation Risk in Covariance

SSRN Electronic Journal, 2007
This paper investigates the estimation risk in covariance. It is known that covariance can be estimated accurately under the i.i.d. normality assumption. However, time varying volatility and non-normality of asset returns can lead to imprecise covariance estimates, which can incur economic loss to a mean variance investor.
David D. Cho, Jeffrey Russell
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Covariance Matrix Estimation

2016
Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
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