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Intuitive covariation estimation [PDF]

open access: yesMemory & 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.
openaire   +2 more sources

ETF Basket-Adjusted Covariance estimation

open access: yesJournal of Econometrics, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Boudt, Kris   +3 more
openaire   +4 more sources

Shrinkage Estimation of the Power Spectrum Covariance Matrix [PDF]

open access: yes, 2008
We seek to improve estimates of the power spectrum covariance matrix from a limited number of simulations by employing a novel statistical technique known as shrinkage estimation.
Adrian C. Pope   +14 more
core   +1 more source

Covariance Estimation: The GLM and Regularization Perspectives [PDF]

open access: yes, 2011
Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data ...
Pourahmadi, Mohsen
core   +5 more sources

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation

open access: yes, 2021
We present two sample-efficient differentially private mean estimators for $d$-dimensional (sub)Gaussian distributions with unknown covariance. Informally, given $n \gtrsim d/α^2$ samples from such a distribution with mean $μ$ and covariance $Σ$, our estimators output $\tildeμ$ such that $\| \tildeμ- μ\|_Σ \leq α$, where $\| \cdot \|_Σ$ is the ...
Brown, Gavin   +4 more
openaire   +2 more sources

Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection

open access: yesSensors, 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
doaj   +1 more source

The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise

open access: yesSensors, 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
doaj   +1 more source

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

open access: yesEntropy, 2019
This paper discusses the effects of introducing nonlinear interactions and noise-filtering to the covariance matrix used in Markowitz’s portfolio allocation model, evaluating the technique’s performances for daily data from seven financial ...
Yaohao Peng   +3 more
doaj   +1 more source

Variational Bayes for Regime-Switching Log-Normal Models

open access: yesEntropy, 2014
The power of projection using divergence functions is a major theme in information geometry. One version of this is the variational Bayes (VB) method. This paper looks at VB in the context of other projection-based methods in information geometry.
Hui Zhao, Paul Marriott
doaj   +1 more source

Gridless DOA estimation with finite rate of innovation reconstruction based on symmetric Toeplitz covariance matrix

open access: yesEURASIP 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
doaj   +1 more source

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