High‐dimensional covariance estimation for Gaussian directed acyclic graph models with given order
Sign Up to like & getrecommendations! Published in 2019 at "Wiley Interdisciplinary Reviews: Computational Statistics"
DOI: 10.1002/wics.1468
Abstract: The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number… read more here.
Keywords: estimation gaussian; dimensional covariance; covariance; covariance matrix ... See more keywords
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
Adaptive Beamforming via Desired Signal Robust Removal for Interference-Plus-Noise Covariance Matrix Reconstruction
Sign Up to like & getrecommendations! Published in 2021 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-020-01481-z
Abstract: To tackle the problem of the desired signal (DS) steering vector mismatch, especially in the situation of direction-of-arrival error and array perturbations, a robust interference-plus-noise covariance matrix (INCM) reconstruction method based upon DS removal is… read more here.
Keywords: plus noise; matrix; covariance matrix; desired signal ... See more keywords
Feature covariance matrix-based dynamic hand gesture recognition
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3719-3
Abstract: AbstractOver the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible… read more here.
Keywords: dynamic hand; feature covariance; hand gesture; covariance matrix ... See more keywords
An uncertainty-set-shrinkage-based covariance matrix reconstruction algorithm for robust adaptive beamforming
Sign Up to like & getrecommendations! Published in 2021 at "Multidimensional Systems and Signal Processing"
DOI: 10.1007/s11045-020-00737-w
Abstract: This paper presents an uncertainty-set-shrinkage (USS) algorithm that aims to reconstruct a precise interference-plus-noise covariance matrix (INCM) and improve the performance of adaptive beamformers when steering vector (SV) mismatch exists. Both of the interference covariance… read more here.
Keywords: covariance matrix; matrix; uncertainty set; set shrinkage ... See more keywords
Large-scale estimation in Rasch models: asymptotic results, approximations of the variance–covariance matrix of item parameters, and divide-and-conquer estimation
Sign Up to like & getrecommendations! Published in 2018 at "Behaviormetrika"
DOI: 10.1007/s41237-018-0046-z
Abstract: Rasch-type item response models are often estimated via a conditional maximum-likelihood approach. This article elaborates on the asymptotics of conditional maximum-likelihood estimates for an increasing number of items, important for modern data settings where a… read more here.
Keywords: large scale; estimation; approximations variance; variance covariance ... See more keywords
Robust adaptive UKF based on SVR for inertial based integrated navigation
Sign Up to like & getrecommendations! Published in 2020 at "Defence Technology"
DOI: 10.1016/j.dt.2019.10.012
Abstract: Abstract Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust… read more here.
Keywords: robust adaptive; adaptive ukf; ukf based; innovation ... See more keywords
Forecasting large covariance matrix with high-frequency data using factor approach for the correlation matrix
Sign Up to like & getrecommendations! Published in 2020 at "Economics Letters"
DOI: 10.1016/j.econlet.2020.109465
Abstract: Abstract We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation… read more here.
Keywords: matrix; correlation matrix; large covariance; covariance matrix ... See more keywords
Modified Extended Kalman Filtering for Nonlinear Stochastic Differential Algebraic Systems
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.2704
Abstract: Abstract The extended Kalman filter (EKF) is one of the most widely used nonlinear filtering technique for a system of differential algebraic equations (DAEs). In this work we propose an alternate EKF approach for state… read more here.
Keywords: covariance; extended kalman; covariance matrix; algebraic equations ... See more keywords
A bearing fault diagnosis scheme with statistical-enhanced covariance matrix and Riemannian maximum margin flexible convex hull classifier.
Sign Up to like & getrecommendations! Published in 2020 at "ISA transactions"
DOI: 10.1016/j.isatra.2020.11.018
Abstract: To achieve more appropriate fault feature representation for bearing, a statistical-enhanced covariance matrix (SECM) is proposed to extract the global-local features and the interaction of them. Besides, three statistical parameters are introduced to SECM to… read more here.
Keywords: fault; enhanced covariance; covariance matrix; fault diagnosis ... See more keywords
Reducing the state space dimension in a large TVP-VAR
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2019.11.006
Abstract: Abstract This paper proposes a new approach to estimating high dimensional time varying parameter structural vector autoregressive models (TVP-SVARs) by taking advantage of an empirical feature of TVP-(S)VARs. TVP-(S)VAR models are rarely used with more… read more here.
Keywords: state; state space; reducing state; tvp var ... See more keywords