Results 101 to 110 of about 547,601 (228)

Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis. [PDF]

J Bus Econ Stat, 2023
Li D, Srinivasan A, Chen Q, Xue L.
europepmc   +1 more source

Improved Speech Spatial Covariance Matrix Estimation for Online Multi-Microphone Speech Enhancement. [PDF]

Sensors (Basel), 2022
Kim M, Cheong S, Song H, Shin JW.
europepmc   +1 more source

Direction of Arrival Estimation for the Coexistence of Uncorrelated and Coherent Signals via Rotation Spatial Differencing Method

IEEE Open Journal of Signal Processing
This paper presents a novel direction of arrival (DOA) estimation method via rotation spatial differencing technique that offers high resolution, robustness, and stable performance. To suppress external environmental noise and improve estimation accuracy,
Peng Luo, Boyu Pang, Defeng Wu, W. Zeng
doaj   +1 more source

DOA Estimation Based on Virtual Array Aperture Expansion Using Covariance Fitting Criterion

Remote Sensing
Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years.
Teng Ma, Minglei Yang, Hangui Zhu, Yule Zhang, Dingsen Zhou   +4 more
doaj   +1 more source

A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors, 2018
Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance.
Weijian Si, Fuhong Zeng, Changbo Hou, Zhanli Peng   +3 more
doaj   +1 more source

Jackknife covariance matrix estimation for observations from mixture

Modern Stochastics: Theory and Applications, 2019
A general jackknife estimator for the asymptotic covariance of moment estimators is considered in the case when the sample is taken from a mixture with varying concentrations of components. Consistency of the estimator is demonstrated.
Rostyslav Maiboroda, Olena Sugakova
doaj   +1 more source

Eigenvalue correction results in face recognition [PDF]

, 2008
Eigenvalues of sample covariance matrices are often used in biometrics. It has been known for several decades that even though the sample covariance matrix is an unbiased estimate of the real covariance matrix [Fukunaga,1990], the eigenvalues of the ...
Hendrikse, Anne, Spreeuwers, Luuk, Veldhuis, Raymond   +2 more
core   +1 more source

Optimal estimation of a large-dimensional covariance matrix under Stein’s loss

Bernoulli, 2018
This paper introduces a new method for deriving covariance matrix estimators that are decision-theoretically optimal within a class of nonlinear shrinkage estimators. The key is to employ large-dimensional asymptotics: the matrix dimension and the sample
Olivier Ledoit, Michael Wolf
semanticscholar   +1 more source

Adaptive high-degree cubature Kalman filter in the presence of unknown measurement noise covariance matrix

The Journal of Engineering, 2019
Here, the authors address the state estimation problem of non-linear systems in the presence of unknown measurement noise (MN) covariance matrix. Recently, a high-degree cubature Kalman filter (HCKF) has been successfully used in the non-linear-state ...
Hong Xu, Huadong Yuan, Huadong Yuan, Keqing Duan, Wenchong Xie, Yongliang Wang   +5 more
doaj   +1 more source

Improved estimation of the covariance matrix of stock returns with an application to portofolio selection [PDF]

This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix.
Michael Wolf, Olivier Ledoit
core