An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation
Sign Up to like & getrecommendations! Published in 2018 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-017-0743-y
Abstract: The extended factored approach (EFA) is believed to be one of the most efficient and practical space–time adaptive processing (STAP) algorithms for clutter suppression in an airborne radar system. However, it cannot effectively work in… read more here.
Keywords: clutter suppression; covariance matrix; persymmetric covariance; matrix estimation ... See more keywords
FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs.
Sign Up to like & getrecommendations! Published in 2022 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2022.106782
Abstract: BACKGROUND AND OBJECTIVE The fundamental matrix estimation is a classic problem in computer vision. The traditional algorithms require high-precision correspondences. However, correspondences in biplanar radiographs are difficult to match accurately. METHODS We propose an end-to-end… read more here.
Keywords: fundamental matrix; matrix; biplanar radiographs; network ... See more keywords
Methodology for O‐D matrix estimation using the revealed paths of floating car data on large‐scale networks
Sign Up to like & getrecommendations! Published in 2020 at "Iet Intelligent Transport Systems"
DOI: 10.1049/iet-its.2019.0684
Abstract: The increasing availability of historical floating car data (FCD) represents a relevant chance to improve the accuracy of model-based traffic forecasting systems. A more precise estimation of origin–destination (O-D) matrices is a critical issue for… read more here.
Keywords: methodology; estimation; floating car; matrix estimation ... See more keywords
Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model
Sign Up to like & getrecommendations! Published in 2018 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2017.1340888
Abstract: ABSTRACT High-frequency financial data allow us to estimate large volatility matrices with relatively short time horizon. Many novel statistical methods have been introduced to address large volatility matrix estimation problems from a high-dimensional Itô process… read more here.
Keywords: high dimensional; estimation; high frequency; volatility ... See more keywords
Fast and accurate inference of gene regulatory networks through robust precision matrix estimation
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac178
Abstract: MOTIVATION Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying Gene Regulatory Network (GRN), and reliably inferring such… read more here.
Keywords: gene regulatory; precision matrix; matrix estimation; robust precision ... See more keywords
Improved Large Dynamic Covariance Matrix Estimation With Graphical Lasso and Its Application in Portfolio Selection
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3031192
Abstract: The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However,… read more here.
Keywords: covariance matrix; matrix estimation; large dynamic; covariance ... See more keywords
Discrete Mumford–Shah on Graph for Mixing Matrix Estimation
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2019.2917518
Abstract: The discrete Mumford–Shah formalism has been introduced for the image denoising problem, allowing to capture both smooth behavior inside an object and sharp transitions on the boundary. In this letter, we propose first to extend… read more here.
Keywords: mumford shah; discrete mumford; mixing matrix; estimation ... See more keywords
Covariance Matrix Estimation Under Positivity Constraints With Application to Portfolio Selection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3226117
Abstract: In this letter we propose a new method to estimate the covariance matrix under the constraint that its off-diagonal elements are non-negative, which has applications to portfolio selection in finance. We incorporate the non-negativity constraint… read more here.
Keywords: matrix estimation; covariance matrix; portfolio selection;
A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2757913
Abstract: A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix… read more here.
Keywords: approach covariance; estimation applications; covariance matrix; matrix estimation ... See more keywords
R-NL: Covariance Matrix Estimation for Elliptical Distributions Based on Nonlinear Shrinkage
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2023.3270742
Abstract: We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and… read more here.
Keywords: estimation elliptical; matrix; covariance matrix; matrix estimation ... See more keywords