Results 141 to 150 of about 3,390,517 (303)

A generalization of Tyler's M-estimators to the case of incomplete data [PDF]

Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established until today. Tyler's M-estimator has been recognized as the 'most robust' M-estimator for the shape matrix of elliptically symmetric
Jaekel, Uwe, Frahm, Gabriel
core  

Oral Actinomyces Species Exposure is Associated with Anxiety‐Like Behavior, Mitochondrial Remodeling, and Altered GABAergic Pathways

Advanced Science, EarlyView.
Oral exposure to cannabis‐associated Actinomyces species, particularly A. meyeri, is associated with anxiety‐like behavior in mice in the absence of detectable bacterial translocation or overt systemic inflammation. These findings support a potential oral‐brain metabolic axis involving candidate metabolites such as arginine and argininosuccinate ...
Tabinda Salman, Zhenwu Luo, Douglas Johnson, Arshad A. Noorani, Zhuang Wan, Bogdan Bordieanu, Zhi‐wei Ye, Rachel D. Penrod, Hongxu Xian, Sylvia Fitting, Peter W. Kalivas, Wei Jiang   +11 more
wiley   +1 more source

CausalCellInfer: Prioritizing Cell‐Type‐Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts

Advanced Science, EarlyView.
Bulk and genotype‐imputed transcriptomes obscure the cellular drivers of complex disease. CausalCellInfer resolves them by integrating invariant marker selection, deep learning deconvolution, and regularized matrix completion. Applied to ∼500 000 UK Biobank participants across 29 traits, it uncovers hallmark cell‐type proportion shifts, including ...
Liangying Yin, Yujia Shi, Ruoyu Zhang, Yong Xiang, Jinghong Qiu, Pak‐Chung Sham, Hon‐Cheong So   +6 more
wiley   +1 more source

A NUV-Based Adaptive Robust Unscented Kalman Filter for Nonlinear State Estimation Under Cyberattacks

IEEE Access
Cyberattacks in Cyber-Physical Systems (CPS) can corrupt sensor measurements, significantly degrading nonlinear state estimation accuracy and causing numerical instability during filtering.
Anpeng Chen, Zhu Ren, Pengcheng Dai, Xue Xiong   +3 more
doaj   +1 more source

On the Variance Covariance Matrix of the Maximum Likelihood Estimator of a Discrete Mixture [PDF]

The estimation of models involving discrete mixtures is a common practice in econometrics, for example to account for unobserved heterogeneity. However, the literature is relatively uninformative about the measurement of the precision of the parameters ...
Gauthier Lanot
core  

µSlicer for Programmable Multimaterial 3D Printing

Advanced Science, EarlyView.
ABSTRACT Nature rarely builds with uniform materials; instead, it achieves exceptional performance by arranging composition and structure across multiple length scales. Mimicking such architectures has become increasingly feasible with additive manufacturing, and especially with multimaterial 3D printing, where complex structures with spatially varying
Daniel H. Meile, Berin Šeta, Md. Tusher Mollah, Yohan Jacquet, Rob Wolfs, Jon Spangenberg   +5 more
wiley   +1 more source

Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix. [PDF]

Visuri et al (2001) proposed and illustrated the use of the affine equivariant rank covariance matrix (RCM) in classical multivariate inference problems.
Croux, Christophe, Ollila, E, Oja, H
core  

ReLink: Reliable Deep Learning for Microscopic Motion Tracking

Advanced Science, EarlyView.
ReLink enables reliable trajectory linking in single‐particle tracking by integrating physics‐informed motion statistics with confidence‐based adaptive filtering. Across diverse simulated and experimental conditions, ReLink robustly reconstructs trajectories and preserves the fidelity of downstream analyses, enabling accurate characterization of ...
Yuan Jiang, Xiaochen Feng, Hongda Wei, Hao Sha, Hongpeng Wang, Min Zhang, Sanyang Han, Yongbing Zhang   +7 more
wiley   +1 more source

Weighted average ensemble for Cholesky-based covariance matrix estimation

Statistical Theory and Related Fields
The modified Cholesky decomposition (MCD) is an efficient technique for estimating a covariance matrix. However, it is known that the MCD technique often requires a pre-specified variable ordering in the estimation procedure.
Xiaoning Kang, Zhenguo Gao, Xi Liang, Xinwei Deng   +3 more
doaj   +1 more source

Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data. [PDF]

This paper considers the problem of estimating, and testing for, a Kronecker product covariance structure of three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data.
Ricardo Leiva, Anuradha Roy
core