Results 41 to 50 of about 3,211,049 (333)
The Intraclass Covariance Matrix [PDF]
Behavior Genetics, 2005 Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives.
openaire +2 more sources
IEEE Access, 2018 The performance of the conventional beamformers degrades in the presence of desired signal in the data samples and array steering vector (ASV) mismatch. Many beamformers have been proposed to improve the performance of standard Capon beamformer. However,
Denis Igambi, Xiaopeng Yang, Babur Jalal
doaj +1 more source
IET Radar, Sonar & Navigation, 2021 To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN)
Hao Xiao, Tong Wang, Cai Wen, Bing Ren
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Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies [PDF]
Evolutionary Computation, 2019 We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the default CMA-ES with the advantages of separable CMA-ES without inheriting ...
Youhei Akimoto, N. Hansen
semanticscholar +1 more source
Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data
Separations, 2021 Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features.
Adam Mieldzioc +2 more
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IET Radar, Sonar & Navigation, 2023 Benefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple‐input multiple‐output (MIMO) sonar applications.
Kuan Fan, Xionghou Liu, Chao Sun
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High-Dimensional Covariance Estimation via Constrained Lq-Type Regularization
Mathematics, 2023 High-dimensional covariance matrix estimation is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields.
Xin Wang +3 more
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Adaptive detection with bounded steering vectors mismatch angle [PDF]
, 2007 We address the problem of detecting a signal of interest (SOI), using multiple observations in the primary data, in a background of noise with unknown covariance matrix.
Besson, Olivier
core +1 more source
Regularization for high-dimensional covariance matrix
Special Matrices, 2016 In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n.
Cui Xiangzhao +5 more
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Estimating the power spectrum covariance matrix with fewer mock samples [PDF]
, 2015 The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues ...
Pearson, David W., Samushia, Lado
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