Results 51 to 60 of about 9,759 (265)
Positive definite estimation of large covariance matrix using generalized nonconvex penalties
This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed.
Fei Wen +3 more
doaj +1 more source
Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization. [PDF]
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models.
Daniel Bartz +4 more
doaj +1 more source
Multimodal Data‐Driven Microstructure Characterization
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova +2 more
wiley +1 more source
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
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
Tuning the Parameters for Precision Matrix Estimation Using Regression Analysis
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
On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator
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
Efficient parallel computation of the estimated covariance matrix [PDF]
Computation of a signal's estimated covariance matrix is an important building block in signal processing, e.g., for spectral estimation. Each matrix element is a sum of products of elements in the input matrix taken over a sliding window. Any given product contributes to multiple output elements, thereby complicating parallelization.
Oded Green +3 more
openaire +2 more sources
Magnetic doping of the topological insulator Bi2Te3 with erbium adatoms induces out‐of‐plane magnetism and breaks time‐reversal symmetry, opening a Dirac gap and driving a Fermi surface transition from hexagonal to star‐of‐David geometry. Microscopy, spectroscopy, and magnetic dichroism reveal atomically controlled magnetic interactions that tailor the
Beatriz Muñiz Cano +18 more
wiley +1 more source

