Results 21 to 30 of about 1,787,003 (299)
SPICE-ML Algorithm for Direction-of-Arrival Estimation
Sensors, 2019 Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal.
Yu Zheng, Lutao Liu, Xudong Yang
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Sparse estimation of a covariance matrix [PDF]
Biometrika, 2011 We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even ...
Jacob Bien, Robert J. Tibshirani
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Intuitive covariation estimation [PDF]
Memory & Cognition, 1986 Six experiments concerned people's ability to estimate the degree and sign of covariation represented in a bivariate distribution of stimuli with which they had just been presented as a series of pairs of stimuli. The stimuli were pairs of numbers, pairs of lines of variable lengths, or word-line pairs.
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Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection
Sensors, 2019 This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance
Naixin Kang, Zheran Shang, Qinglei Du
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Nonparametric estimation of covariance functions by model selection [PDF]
, 2009 We propose a model selection approach for covariance estimation of a stochastic process. Under very general assumptions, observing i.i.d replications of the process at fixed observation points, we construct an estimator of the covariance function by ...
Muniz Alvarez, Lilian +9 more
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Covariate assisted screening and estimation
The Annals of Statistics, 2014 Consider a linear model Y = Xβ + z, where X = Xn;p and z ≈ N(0; In). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series [11] and change point problem [2], we are primarily interested in the case where the Gram matrix G = X1X is
Ke, Zheng Tracy +2 more
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EURASIP Journal on Advances in Signal Processing, 2020 Due to the rapid development and wide application of compressed sensing and sparse reconstruction theory, there exists a series of sparsity-based methods for the antenna sensor array direction of arrival (DOA) estimation with excellent performance ...
Tao Chen, Lin Shi, Yongzhi Yu
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The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise
Sensors, 2022 Due to the discontinuity of ocean waves and mountains, there are often multipath propagation effects and obvious pulse characteristics in low-altitude detection.
Bin Lin +4 more
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
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SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data [PDF]
, 2011 In ``-omic data'' analysis, information on the structure of covariates are broadly available either from public databases describing gene regulation processes and functional groups such as the Kyoto encyclopedia of genes and genomes (KEGG), or from ...
Tenenhaus, Arthur +3 more
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