Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants

Reinhard Furrer; Thomas Bengtsson

doi:10.1016/j.jmva.2006.08.003

Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2006.08.003 ◽

2007 ◽

Vol 98 (2) ◽

pp. 227-255 ◽

Cited By ~ 194

Author(s):

Reinhard Furrer ◽

Thomas Bengtsson

Keyword(s):

Kalman Filter ◽

Covariance Matrices ◽

High Dimensional

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Large Sample Covariance Matrices and High-Dimensional Data Analysis

10.1017/cbo9781107588080 ◽

2015 ◽

Cited By ~ 26

Author(s):

Jianfeng Yao ◽

Shurong Zheng ◽

Zhidong Bai

Keyword(s):

Data Analysis ◽

High Dimensional Data ◽

Covariance Matrices ◽

High Dimensional ◽

Large Sample ◽

Sample Covariance Matrices ◽

Sample Covariance ◽

High Dimensional Data Analysis

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Structure selection of noise covariance matrices for linear Kalman filter design

2020 European Control Conference (ECC) ◽

10.23919/ecc51009.2020.9143850 ◽

2020 ◽

Author(s):

Federico Bianchi ◽

Simone Formentin ◽

Luigi Piroddi

Keyword(s):

Kalman Filter ◽

Filter Design ◽

Covariance Matrices ◽

Structure Selection ◽

Noise Covariance ◽

Selection Of

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Dynamic Principal Component CAW Models for High-Dimensional Realized Covariance Matrices

SSRN Electronic Journal ◽

10.2139/ssrn.2888520 ◽

2016 ◽

Author(s):

Bastian Gribisch ◽

Michael Stollenwerk

Keyword(s):

Principal Component ◽

Covariance Matrices ◽

High Dimensional ◽

Realized Covariance

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Sparse Estimation of High-Dimensional Inverse Covariance Matrices with Explicit Eigenvalue Constraints

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00351-y ◽

2021 ◽

Author(s):

Yun-Hai Xiao ◽

Pei-Li Li ◽

Sha Lu

Keyword(s):

Covariance Matrices ◽

High Dimensional ◽

Sparse Estimation

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Empirical likelihood test for the equality of several high-dimensional covariance matrices

Science China Mathematics ◽

10.1007/s11425-019-1688-1 ◽

2021 ◽

Author(s):

Guili Liao ◽

Liang Peng ◽

Rongmao Zhang

Keyword(s):

Empirical Likelihood ◽

Covariance Matrices ◽

High Dimensional

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Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models

Scandinavian Journal of Statistics ◽

10.1111/sjos.12508 ◽

2020 ◽

Author(s):

Monika Bours ◽

Ansgar Steland

Keyword(s):

Time Series ◽

Linear Time ◽

Factor Models ◽

Covariance Matrices ◽

High Dimensional ◽

Large Sample

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Global one-sample tests for high-dimensional covariance matrices

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2021.1882459 ◽

2021 ◽

pp. 1-23

Author(s):

Xiaoyi Wang ◽

Baisen Liu ◽

Ning-Zhong Shi ◽

Guo-Liang Tian ◽

Shurong Zheng

Keyword(s):

Covariance Matrices ◽

High Dimensional

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Ridge estimation of inverse covariance matrices from high-dimensional data

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2016.05.012 ◽

2016 ◽

Vol 103 ◽

pp. 284-303 ◽

Cited By ~ 28

Author(s):

Wessel N. van Wieringen ◽

Carel F.W. Peeters

Keyword(s):

High Dimensional Data ◽

Covariance Matrices ◽

High Dimensional ◽

Ridge Estimation

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Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2014.06.001 ◽

2014 ◽

Vol 131 ◽

pp. 149-162 ◽

Cited By ~ 15

Author(s):

Alexis Hannart ◽

Philippe Naveau

Keyword(s):

Covariance Matrices ◽

High Dimensional ◽

New Look

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Weighted Statistic in Detecting Faint and Sparse Alternatives for High-Dimensional Covariance Matrices

Journal of the American Statistical Association ◽

10.1080/01621459.2015.1122602 ◽

2017 ◽

Vol 112 (517) ◽

pp. 188-200 ◽

Cited By ~ 1

Author(s):

Qing Yang ◽

Guangming Pan

Keyword(s):

Covariance Matrices ◽

High Dimensional

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