Results 61 to 70 of about 108,134 (338)
On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator
Journal of Mathematics, 2023 This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE ...
Abdenour Hamdaoui +5 more
doaj +1 more source
Mathematics, 2022 The estimation of the covariance matrix is an important topic in the field of multivariate statistical analysis. In this paper, we propose a new estimator, which is a convex combination of the linear shrinkage estimation and the rotation-invariant ...
Yan Zhang +3 more
doaj +1 more source
Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators [PDF]
, 2010 Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary non-parametric maximum likelihood density estimators are shown to be asymptotically normal.
Gach, Florian, Pötscher, Benedikt M.
core +2 more sources
Spatial Sign Correlation [PDF]
, 2014 A new robust correlation estimator based on the spatial sign covariance matrix (SSCM) is proposed. We derive its asymptotic distribution and influence function at elliptical distributions.
Dürre, Alexander +2 more
core +3 more sources
Advanced Materials, EarlyView.Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje +7 more
wiley +1 more source
Tuning the Parameters for Precision Matrix Estimation Using Regression Analysis
IEEE Access, 2019 Precision matrix, i.e., inverse covariance matrix, is widely used in signal processing, and often estimated from training samples. Regularization techniques, such as banding and rank reduction, can be applied to the covariance matrix or precision matrix ...
Jun Tong +4 more
doaj +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Appoximation-assisted [sic] estimation of eigenvectors under quadratic loss [PDF]
, 2005 Improved estimation of eigen vector of covariance matrix is considered under uncertain prior information (UPI) regarding the parameter vector. Like statistical models underlying the statistical inferences to be made, the prior information will be ...
Ahmed, S.E.
core
A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems
, 2017 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 ...
Aubry, Augusto +2 more
core +1 more source
Covariance Matrix Estimation in Massive MIMO [PDF]
IEEE Signal Processing Letters, 2018 submitted to IEEE Signal Processing ...
David Neumann +2 more
openaire +2 more sources