Article
Computer Science, Interdisciplinary Applications
Alexander Smith, Benjamin Laubach, Ivan Castillo, Victor M. Zavala
Summary: This article explores the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices. SPD matrices, commonly used in chemical engineering and image analysis, can benefit from techniques that exploit the properties of Riemannian manifold in tasks such as classification and dimensionality reduction.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Agronomy
Josafhat Salinas-Ruiz, Sandra Luz Hernandez-Valladolid, Juan Valente Hidalgo-Contreras, Juan Manuel Romero-Padilla
Summary: Mixed models are useful for analyzing sugarcane field trials and determining the best model for estimating cane stalk yield of sugarcane varieties. The research highlights the need for improving the process of finding an appropriate mixed model for more accurate recommendations in the sugarcane industry.
Article
Engineering, Aerospace
Rick H. Yuan, Clark N. Taylor, Scott L. Nykl, Clark Taylor
Summary: One of the fundamental problems in robotics and navigation is estimating the relative pose between an external object and the observer. This paper introduces a novel method for estimating uncertainty from sensed data.
NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION
(2022)
Article
Engineering, Electrical & Electronic
Elias Raninen, Esa Ollila
Summary: The article focuses on the estimation of covariance matrices of multiple classes through the use of regularized SCM estimators. By coupling the regularization towards the pooled SCM and scaled identity matrix, the proposed techniques show promising MSE performance in scenarios where class populations follow elliptical distributions. The coupled RSCMs demonstrate comparable performance to cross-validation but with significantly faster computation time when applied on real data sets.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Geosciences, Multidisciplinary
Joshua Chun Kwang Lee, Javier Amezcua, Ross Noel Bannister
Summary: Hybrid ensemble-variational data assimilation methods are widely used in the mid-latitudinal context, but their benefits in the tropical context have been less explored. This study introduces and improves the hybrid ensemble-variational DA method in a tropical configuration of a simplified fluid dynamics model. The algorithm includes localization and weighting parameters, and an ensemble system is designed to generate ensemble perturbations. Sensitivity tests using observing system simulation experiments show that the hybrid method performs well with certain weighting configurations.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Statistics & Probability
Jiaxin Qiu, Zeng Li, Jianfeng Yao
Summary: The paper derives the asymptotic normality for a large family of eigenvalue statistics of a general sample covariance matrix under the ultrahigh-dimensional setting. Based on this result, the covariance matrix test problem is extended to the new ultra-high-dimensional context and applied to test a matrix-valued white noise. Simulation experiments are conducted to investigate the finite-sample properties of the general asymptotic normality of eigenvalue statistics and the two developed tests.
ANNALS OF STATISTICS
(2023)
Article
Mathematics
Jin Zou, Dong Han
Summary: This paper focuses on the vital role of Gini covariance in analyzing the relationship between random variables with heavy-tailed distributions. The authors establish the Gini-Yule-Walker equation to estimate the transition matrix of high-dimensional periodic vector autoregressive processes, and apply this method to study the Granger causality of heavy-tailed PVAR processes, with results showing robust transfer matrix estimation leads to sign consistency in the value of Granger causality. The effectiveness of the proposed method is verified through both synthetic and real data.
Article
Engineering, Aerospace
Lifei Zhang, Shaoping Wang, Maria Sergeevna Selezneva, Konstantin Avenirovich Neusypin
Summary: This study investigates the features of carrier-based aircraft's navigation systems during the approach and landing phases. A new adaptive Kalman filter is proposed to improve the accuracy of the INS/GNSS integrated navigation system by considering unknown state noise covariance Q. The results of simulations and semi-physical experiments demonstrate that the application of the proposed adaptive Kalman filter can ensure higher estimation accuracy of the state variables.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Physics, Mathematical
Maria Stella Adamo, Luca Giorgetti, Yoh Tanimoto
Summary: The focus of this work is on the two-dimensional full conformal field theories with specific characteristics of chiral components. By constructing the Hilbert space structure and Wightman fields, we demonstrate the features of the theory and propose a method for local extension.
COMMUNICATIONS IN MATHEMATICAL PHYSICS
(2023)
Article
Biochemistry & Molecular Biology
Magdalena Fabjanowicz, Vasil Simeonov, Marcin Frankowski, Wojciech Wojnowski, Justyna Plotka-Wasylka
Summary: This study investigated the correlation between specific compounds in cool-climate country wines, analyzed their elemental composition, and used chemometric analysis. The results revealed specific correlations between different compounds in cool-climate wines.
Article
Computer Science, Artificial Intelligence
Yante Li, Xiaohua Huang, Guoying Zhao
Summary: This paper focuses on AU detection in micro-expressions, proposing a novel method that utilizes self high-order statistics of spatio-wise and channel-wise features as spatial and channel attentions. By leveraging rich relationship information of facial regions through a spatial attention module, the method aims to increase AU detection robustness on limited micro-expression samples. Additionally, exploring high-order statistics for capturing subtle regional changes on face to obtain more discriminative AU features contributes to the improved performance of the proposed approach on CASME II, CASME, and SAMM datasets.
Article
Statistics & Probability
Lewis R. Blake, Emilio Porcu, Dorit M. Hammerling
Summary: Gaussian Processes are powerful tools for spatial data modeling. In this work, we focus on specifying the symmetric and positive definite covariance function, which has traditionally been defined and used in Euclidean space. However, considering Earth's geometry becomes increasingly important when dealing with data collected from the globe. We survey recent developments related to constructing nonstationary covariance functions on spheres and provide three general forms for families of parametric nonstationary covariance functions.
Article
Statistics & Probability
Zhixiang Zhang, Shurong Zheng, Guangming Pan, Ping-Shou Zhong
Summary: We consider high-dimensional spiked sample covariance models and prove that the leading sample spiked eigenvalues and linear spectral statistics are asymptotically independent when the sample size and dimension are proportional. We also establish the central limit theorem of the leading sample spiked eigenvalues without assuming block diagonal structure on the population covariance matrix. Moreover, we propose consistent estimators for the L-4 norm of the spiked population eigenvectors. Based on these results, we develop a new statistic to test the equality of two spiked population covariance matrices, and numerical studies show its superiority over existing methods.
ANNALS OF STATISTICS
(2022)
Article
Statistics & Probability
Quan Vu, Andrew Zammit-Mangion, Noel Cressie
Summary: This article introduces a new class of nonstationary and asymmetric multivariate spatial covariance models by modeling the simpler and more familiar stationary and symmetric multivariate covariances on a warped domain. The warping function is modeled as a composition of several simple injective warping functions in a deep-learning framework. The validity of the covariance model is guaranteed by construction. The utility of this new class of models is demonstrated through two data illustrations.
Article
Economics
Xinxin Yang, Xinghua Zheng, Jiaqi Chen
Summary: Tests for high-dimensional covariance matrices based on a generalized elliptical model are developed, without assuming specific parametric distributions or involving data kurtosis. These tests can be used to test uncorrelatedness among idiosyncratic returns, demonstrating their flexibility and applicability.
JOURNAL OF ECONOMETRICS
(2021)