Results 31 to 40 of about 1,787,003 (299)
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
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Agnostic Estimation of Mean and Covariance [PDF]
2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), 2016 We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $η$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type.
Kevin A. Lai +2 more
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Automatic positive semidefinate HAC covariance matrix and GMM estimation [PDF]
, 2005 This paper proposes a new class of heteroskedastic and autocorrelation consistent (HAC) covariance matrix estimators. The standard HAC estimation method reweights estimators of the autocovariances.
Smith, Richard J.
core +1 more source
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
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Variational Bayes for Regime-Switching Log-Normal Models
Entropy, 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
IEEE Access, 2021 In order to solve the problem that the gridless DOA estimation algorithms based on generalized finite rate of innovation (FRI) signal reconstruction model are not suitable for two-dimensional DOA estimation using planar array, a separable gridless DOA ...
Kunda Wang, Lin Shi, Tao Chen
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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
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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
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Variational Bayesian Parameter Estimation Techniques for the General Linear Model
Frontiers in Neuroscience, 2017 Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data.
Ludger Starke +3 more
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Estimation of a covariance matrix with zeros [PDF]
Biometrika, 2007 25 ...
Chaudhuri, S. +2 more
openaire +3 more sources