Results 71 to 80 of about 1,787,003 (299)

A Knowledge-Aided Robust Ensemble Kalman Filter Algorithm for Non-Linear and Non-Gaussian Large Systems

Frontiers in Applied Mathematics and Statistics, 2022
This work proposes a robust and non-Gaussian version of the shrinkage-based knowledge-aided EnKF implementation called Ensemble Time Local H∞ Filter Knowledge-Aided (EnTLHF-KA).
Santiago Lopez-Restrepo, Santiago Lopez-Restrepo, Santiago Lopez-Restrepo, Andres Yarce, Andres Yarce, Andres Yarce, Nicolás Pinel, O. L. Quintero, Arjo Segers, A. W. Heemink   +9 more
doaj   +1 more source

On Purely Private Covariance Estimation

CoRR
ALT 2026; equal ...
Tommaso d'Orsi, Gleb Novikov
openaire   +3 more sources

A blocking and regularization approach to high dimensional realized covariance estimation [PDF]

, 2009
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix ...
Hautsch, Nikolaus, Lada M. Kyj, Oomen, Roel C.A., Roel C. A. Oomen, Nikolaus Hautsch, Kyj, Lada M., Oomen, Roel C. A.   +6 more
core   +1 more source

Real‐World Safety and Effectiveness of JAK Inhibitors in Systemic Sclerosis: A Propensity‐Matched Study From the EUSTAR Cohort

Arthritis Care &Research, EarlyView.
Objective JAK inhibitors (JAKi) have shown promising effects in early‐phase studies of systemic sclerosis (SSc). We aimed to assess the safety and explore the effectiveness of JAKi compared to conventional immunosuppressants in SSc. Methods A longitudinal retrospective study of the European Scleroderma Trials and Research Group (EUSTAR) cohort was ...
Stefano Di Donato, Marie‐Elise Truchetet, Marco Minerba, Oliver Distler, Juan José Alegre Sancho, Yolanda Braun‐Moscovici, Christina Bergmann, Petros Sfikakis, Jeska K. de Vries‐Bouwstra, Murray Baron, Silvia Bellando‐Randone, Lorenzo Dagna, Christopher P. Denton, Madelon C. Vonk, Vanessa Smith, Ivan Castellvi, Gabriela Riemekasten, Andra Balanescu, Masataka Kuwana, Maria De Santis, Kamal Solanki, Anastas Batalov, Vahan Mukuchyan, Marco Matucci‐Cerinic, Yannick Allanore, Francesco Del Galdo, Michael Hughes, EUSTAR Collaborators   +27 more
wiley   +1 more source

DOA Estimation of Completely Polarized Signals by One-Bit Cross-Dipole Arrays

IEEE Access
The one-bit cross-dipole array employs one-bit quantization to reduce the sampling system overhead while extracting polarization information from electromagnetic signals, thereby lowering the system complexity of the polarization array.
Yu Wang, Qian Cao, Zhiyong Cheng, Di Song, Zhenyu Liu, Zheng Lu   +5 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Masked Toeplitz covariance estimation

CoRR, 2017
The problem of estimating the covariance matrix $Σ$ of a $p$-variate distribution based on its $n$ observations arises in many data analysis contexts. While for $n>p$, the classical sample covariance matrix $\hatΣ_n$ is a good estimator for $Σ$, it fails in the high-dimensional setting when $n\ll p$.
Maryia Kabanava, Holger Rauhut
openaire   +2 more sources

Generalized sparse covariance-based estimation [PDF]

Signal Processing, 2018
In this work, we extend the sparse iterative covariance-based estimator (SPICE), by generalizing the formulation to allow for different norm constraints on the signal and noise parameters in the covariance model. For a given norm, the resulting extended SPICE method enjoys the same benefits as the regular SPICE method, including being hyper-parameter ...
Johan Swärd, Stefan Ingi Adalbjornsson, Andreas Jakobsson   +2 more
openaire   +2 more sources