Results 121 to 130 of about 1,787,003 (299)

A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The polarimetric synthetic aperture radar tomography (TomoSAR) technique has proven to be a highly promising cutting-edge microwave remote sensing technique for obtaining forest vertical structure parameters because of its ability in three-dimensional ...
Youjun Wang, Xing Peng, Qinghua Xie, Yanan Du, Bing Zhang, Xiaomin Luo, Weijun Yan, Xinwu Li   +7 more
doaj   +1 more source

Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models [PDF]

Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data.
Christopher K. Carter, Helen Armstrong, Robert Kohn, Kevin K. F. Wong   +3 more
core  

Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations

Advanced Science, EarlyView.
Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford, Matthew Tamasi, Elena Di Mare, Adam J. Gormley   +3 more
wiley   +1 more source

A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations [PDF]

We extend range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator motivated by a key financial economic consideration, the absence of arbitrage, in addition to statistical considerations ...
Michael W. Brandt, Francis X. Diebold
core   +2 more sources

High‐Dimensional Covariance Estimation From a Small Number of Samples

Journal of Advances in Modeling Earth Systems
We synthesize knowledge from numerical weather prediction, inverse theory, and statistics to address the problem of estimating a high‐dimensional covariance matrix from a small number of samples.
David Vishny, Matthias Morzfeld, Kyle Gwirtz, Eviatar Bach, Oliver R. A. Dunbar, Daniel Hodyss   +5 more
doaj   +1 more source

Honey, I shrunk the sample covariance matrix [PDF]

The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer.
Michael Wolf, Olivier Ledoit
core  

A Pan‐Methylome Framework for Population‐Scale Bacterial Epigenomics

Advanced Science, EarlyView.
A scalable quantitative framework unlocks population‐level comparative epigenomics in bacteria. By transforming site‐level data into standardized traits, this approach reconstructs methylation‐informed phylogenies and defines the core epigenome.
Bin Ma, Huimin Gong, Ran Zhuo, Zaixiao Rao, Yanxi Wan, Mingyang Wang, Zahra Zeinalzadeh, Yuan Gao, Aohan Guan, Xinrun Li, Wanting Chen, Xiliang Wang, Yun Wan, Sihua Zhang, Yuncai Xiao, Qi Huang, Paul R. Langford, Xiangru Wang, Rui Zhou, Rui Luo, Hongbo Zhou, Chengliang Zhu, Hui Jin   +22 more
wiley   +1 more source

Measurement noise covariance estimation in Gaussian filters: an online Bayesian solution

EURASIP Journal on Advances in Signal Processing
Gaussian filtering provides a Bayesian approach to dynamic state estimation, but requires precise statistical information about observation noise. When this information is unavailable, it is necessary to estimate the measurement noise covariance based on
Gerald LaMountain, Jordi Vilà-Valls, Pau Closas   +2 more
doaj   +1 more source

Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order [PDF]

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter.
Wouter J. den Haan, Andrew T. Levin
core  

Human‐Guided Bayesian Optimization Enables High‐Throughput Laser Annealing of Mesoporous SiOx Anodes for Lithium‐Ion Batteries

Advanced Science, EarlyView.
An empirical‐aided active learning framework is developed to optimize high‐throughput laser‐induced photothermal annealing of silicon suboxide anodes. By integrating probabilistic machine learning with empirical domain knowledge, this approach achieves optimal electrochemical performance using limited experiments.
Chaeyoung Park, Yeongje Lee, Sunho Jeong, Eungkyu Lee   +3 more
wiley   +1 more source