Results 21 to 30 of about 134,557 (305)
Robust covariance estimation for data fusion from multiple sensors [PDF]
, 2011 This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics.
Lazarus, Samuel B. +5 more
core +1 more source
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.
openaire +2 more sources
Estimation of a Multiplicative Covariance Structure [PDF]
SSRN Electronic Journal, 2016 We consider a Kronecker product structure for large covariance matrices, which has the feature that the number of free parameters increases logarithmically with the dimensions of the matrix. We propose an estimation method of the free parameters based on the log linear property of this structure, and also a Quasi-Likelihood method.
Hafner, Christian M. +2 more
openaire +2 more sources
Estimator of Agreement with Covariate Adjustment
Journal of Agricultural, Biological and Environmental Statistics, 2023 AbstractThe parameter $$\kappa $$ κ is a general agreement structure used across many fields, such as medicine, machine learning and the pharmaceutical industry. A popular estimator for $$\kappa $$ κ is Cohen’s $$\kappa $$ κ ; however, this estimator
Katelyn A. McKenzie, Jonathan D. Mahnken
openaire +2 more sources
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
doaj +1 more source
Graph Sampling for Covariance Estimation [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2017 In this paper the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of ...
Sundeep Prabhakar Chepuri, Geert Leus
openaire +3 more sources
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
core +1 more source
Entropy, 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
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
openaire +3 more sources
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
doaj +1 more source