Results 241 to 250 of about 534,560 (282)

Threshold Selection for Covariance Estimation

Biometrics, 2019
Abstract Thresholding is a regularization method commonly used for covariance estimation, which provides consistent estimators if the population covariance satisfies certain sparsity condition (Bickel and Levina, 2008a; Cai and Liu, 2011). However, the performance of the thresholding estimators heavily depends on the threshold level.
Yumou Qiu, Janaka S. S. Liyanage
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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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