High‐Dimensional Covariance Estimation From a Small Number of Samples
Journal of Advances in Modeling Earth SystemsWe 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 +5 more
doaj +1 more source
Semiparametric estimation with missing covariates
Journal of Multivariate Analysis, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation
Advanced Science, EarlyView.This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar +10 more
wiley +1 more source
Improved HAC Covariance Matrix Estimation Based on Forecast Errors [PDF]
We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators.
Yu-Wei Hsieh, Chung-Ming Kuan
core
Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market [PDF]
This paper examines effects of realized covariance matrix estimators based on high-frequency data on large-scale minimum-variance equity portfolio optimization.
Masato Ubukata
core +2 more sources
Measurement noise covariance estimation in Gaussian filters: an online Bayesian solution
EURASIP Journal on Advances in Signal ProcessingGaussian 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 +2 more
doaj +1 more source
A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces
Advanced Science, EarlyView.A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren +5 more
wiley +1 more source
Heteroskedasticity-Consistent Estimation of the Variance-Covariance Matrix for the Almost Ideal Demand System [PDF]
In this note I demonstrate the previously overlooked fact that if the AIDS aggregate demand model is constructed as the aggregation of individual consumer demands, then the error structure for any individual equation is necessarily heteroskedastic unless
Melvyn A. Fuss
core
Regularized estimation of large-scale gene association networks using graphical Gaussian models [PDF]
, 2009 Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial ...
Schäfer, Juliane +10 more
core +1 more source
Application of regularized covariance matrices in logistic regression and portfolio optimization
Scientific ReportsCovariance estimation has widespread applications in various fields such as logistic regression and portfolio optimization. However, in high-dimensional or small-sample scenarios, traditional covariance matrix estimation often encounters the problem of ...
Fang Sun, Xiaoqing Huang
doaj +1 more source